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Am J Physiol Heart Circ Physiol 286: H1170-H1176, 2004. First published November 20, 2003; doi:10.1152/ajpheart.00418.2003
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Sleep-related sympathovagal imbalance in SHR

Terry B. J. Kuo,1,2,3 Ching J. Lai,1,2 Fu-Zen Shaw,1,2 Chi-Wan Lai,4 and Cheryl C. H. Yang1,2

1Institute of Neuroscience and 2Department of Physiology, Tzu Chi University, Hualien 970; 3Department of Neurology, Tzu Chi Buddhist General Hospital, Hualien 970; and 4Department of Neurology, Koo Foundation Sun Yat-Sen Cancer Center, Taipei 112, Taiwan

Submitted 6 May 2003 ; accepted in final form 13 November 2003


   ABSTRACT
TOP
 ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The role of the autonomic nervous system in spontaneous hypertension during each stage of the sleep-wake cycle remains unclear. The present study attempted to evaluate the differences in cardiac autonomic modulations among spontaneously hypertensive rats (SHR), normotensive Wistar-Kyoto rats (WKY), and Sprague-Dawley rats (SD) across sleep-wake cycles. Continuous power spectral analysis of electroencephalogram, electromyogram, and heart rate variability was performed in unanesthetized free moving rats during daytime sleep. Frequency-domain analysis of the stationary R-R intervals (RR) was performed to quantify the high-frequency power (HF), low-frequency power (LF)-to-HF ratio (LF/HF), and normalized LF (LF%) of heart rate variability. WKY and SD had similar mean arterial pressure, which is significantly lower than that of SHR during active waking, quiet sleep, and paradoxical sleep. Compared with WKY and SD, SHR had lower HF but similar RR, LF/HF, and LF% during active waking. During quiet sleep, SHR developed higher LF/HF and LF% in addition to lower HF. SHR ultimately exhibited significantly lower RR accompanied with higher LF/HF and LF% and lower HF during paradoxical sleep compared with WKY. We concluded that significant cardiac sympathovagal imbalance with an increased sympathetic modulation occurred in SHR during sleep, although it was less evident during waking.

autonomic nervous system; heart rate variability; electroencephalogram; paradoxical sleep; quiet sleep; spontaneously hypertensive rats

ENHANCED BASAL AND ENVIRONMENTALLY EVOKED sympathetic neural activity have been implicated in the development and maintenance of spontaneous hypertension observed in experimental animal models, such as spontaneously hypertensive rats (SHR). In support of this hypothesis, studies of anesthetized SHR have revealed enhanced basal sympathetic nerve activity (8), augmented pressor (12), or sympathoexcitatve response to stimuli (17, 23) compared with normotensive Wistar-Kyoto rats (WKY). In freely moving humans or animals, the applications of heart rate variability (HRV) have recently gained popularity in quantifying autonomic functions noninvasively. On the basis of such techniques, however, previous studies have had controversial results regarding the representation of autonomic nervous system (ANS) activities in hypertensive and normotensive subjects. Some evidence has suggested that hypertensive humans or animals have higher (6, 9, 12, 14), similar (18), or even lower (5) cardiac sympathetic function compared with their normotensive controls. In addition, studies comparing parasympathetic functions between SHR and WKY have also led to controversial results (2, 4, 18). It should be noted that most of the previous comparisons were made without a detailed classification of conscious states such as waking and sleep.

Sleep comprises an important part of human life as well as other mammals' lives. Our present knowledge about the changes in autonomic functions across the sleep-wake cycle associated with spontaneous hypertension, however, is still limited. Disruption of normal sleep may contribute to hypertension (22). On the other hand, the ANS persistently plays its role in the maintenance of life not only in the conscious state but also in the sleep state. Abnormalities in autonomic functioning have also been related to various diseases, especially hypertension (6, 9, 12, 14). Evidence has shown autonomic functions in the waking state may not parallel to those in the sleep state (21). Previous studies from our laboratory have demonstrated a simple and quantitative analysis to explore the interaction between cerebral cortical and autonomic functions during sleep (28) that did not utilize the specific terminology of different waveforms used in conventional sleep staging (19). The methodology was based on simultaneous power spectral analyses of electroencephalogram (EEG), electromyogram (EMG), and HRV signals and was readily applied in the study of rats (29). With the application of such techniques, the present study was designed to test whether a classification of sleep-wake states using EEG and EMG may provide a deeper insight into the cardiac autonomic modulations associated with spontaneous hypertension in rats.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Preparation of animals. Experiments were carried out on adult male SHR (n = 10), WKY (n = 10), and Sprague-Dawley rats (SD) (n = 10). They were raised in a sound-attenuated room with a 12:12-h light-dark cycle (06:00–18:00 lights on) and at appropriate temperature (22 ± 2°C) and humidity (40–70%) control. The detailed surgical procedure for electrode implantation has been described previously (24). At the time of electrode implantation, rats were 8–10 wk old. Under pentobarbital anesthesia (50 mg/kg ip), each rat was placed in a standard stereotaxic apparatus where electrodes for parietal EEG, nuchal EMG, and electrocardiogram (ECG) were implanted. For recordings of arterial pressure signals, the rat was instrumented with a telemetry transmitter (TA11PA-C40, Data Sciences; St. Paul, MN). Through a midline incision, the abdominal aorta was exposed and clamped off with surgical clamps. The tip of the probe catheter was inserted rostrally through a small hole in the abdominal wall and fixed in position with a drop of tissue glue. The body of the transmitter was positioned in the abdominal cavity and sutured to the inside of the muscle wall. After surgery, the rats were given antibiotics (chlortetracycline) and housed individually in cages for recovery. One week after surgery, animals were placed individually in clear acrylic chambers so that their behaviors could be observed and recorded with a video recorder. To allow the rats to habituate to the experimental apparatus, each animal was placed in the recording environment at least two times (1 h/day) before being tested. On the day of the recording, a 30-min period was allowed for the rat to become familiar with the chamber. Both electrophysiological and video signals were then simultaneous recorded for 6 h (10:30–16:30) in a sound-attenuated room. The experimental protocol was approved by the Institutional Animal Care and Use Committee of Tzu Chi University.

Data acquisition and storage. EEG, EMG, and ECG signals were amplified 10,000-fold but with different selections for filter bandwidths. The EEG was filtered with 0.3–70 Hz, the EMG with 100–500 Hz, and the ECG with 10–100 Hz (24). These bioelectrical and arterial pressure signals were relayed to a 12-bit analog-digital converter (PCL-818L, Advantech) connected to an IBM personal computer-compatible computer. EEG, EMG, ECG, and arterial pressure signals were synchronously digitized but at different sampling rates (256, 1,024, 1,024, and 1,024 Hz, respectively). The acquired data were analyzed on-line but were simultaneously stored on a hard disk for subsequent off-line verification.

Digital signal processing. Five hours (11:30–16:30) of the recorded signals were subject to the following analysis. Digital signal processing of the bioelectric signals was similar to our previous studies (28, 29). The computer program was written in Pascal (Borland Pascal 7.0). Mean arterial pressure (MAP) was obtained by digital integration of the arterial pressure signals. Preprocessing of the ECG signals was designed according to the recommended procedures (26) as detailed in our previous investigations (11, 29). In brief, the computer algorithm identified each QRS complex and rejected each ventricular premature complex or noise according to its likelihood in a standard QRS template. Stationary R-R intervals (RR) were resampled and interpolated at a rate of 64 Hz to provide continuity in the time domain. The sampling rate of EEG signals was also reduced to 64 Hz.

Power spectral analysis. The EEG and RR signals to be analyzed were truncated into successive 16-s (1,024 points) time segments (windows or epochs) with 50% overlapping. A Hamming window was applied to each time segment to attenuate the leakage effect (10). Our algorithm then estimated the power density of the spectral components based on the fast Fourier transform (FFT). The resulting power spectrum was corrected for attenuation resulting from sampling and application of the Hamming window. The EMG signals to be analyzed were truncated into successive 2-s (2,048 points) time segments without overlap. They then underwent FFT after application of the Hamming window. Eight successive EMG spectra (total 16 s) were averaged to achieve synchronization between the EEG and RR spectra.

For each 16-s time segment, the total power (TP), low-frequency power (LF; 0.06–0.6 Hz), high-frequency power (HF; 0.6–2.4 Hz) of the RR spectrogram (7, 29), delta wave power (0.5–4 Hz) of the EEG spectrogram (24, 29), and the power of the EMG spectrogram (200–500 Hz) were quantified by the method of integration, i.e., calculation of the area of the power spectral density between two specified frequencies. The LF-to-HF ratio (LF/HF), normalized HF (HF%), and normalized LF (LF%) were also calculated (26). We additionally quantified the mean power frequency (MPF) of the EEG spectrogram (16) using the following equation

where f is any given frequency, fo is the lower cutoff frequency, fc is the upper cutoff frequency, and PSD(f) is the power spectral density of a given frequency.

Sleep pattern analysis. For each time segment, we defined its sleep stage as active waking (AW) if the corresponding MPF was greater than the threshold of MPF (TMPF) and the EMG power was greater than the threshold of EMG power (TEMG); as quiet sleep (QS) if the corresponding MPF was less than TMPF and the EMG power was less than TEMG; and as paradoxical sleep (PS) if the corresponding MPF was greater than TMPF and the EMG power was less than TEMG. The thresholds were defined manually by the operator.

Statistical analysis. TP, HF, LF, LF/HF, HF%, and LF% of the RR spectrogram and delta wave power of the EEG spectrogram were quantified. These parameters, except HF% and LF%, were logarithmically transformed to correct for the skewness of distribution (11). Differential effects of the three animal groups (WKY, SD, and SHR) and the three sleep-wake states (AW, QS, and PS) on these HRV parameters were assessed using two-way ANOVA. When indicated by a significant F-statistic, regional differences were isolated using post hoc comparisons with Fisher's least-significant difference test. Statistical significance was assumed for P < 0.05. Values are expressed as means ± SE.


   RESULTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Like WKY, SHR had similar cyclic changes of EEG and RR spectra along daytime recording (Fig. 1). The more prominent HF of WKY was especially noteworthy. The cardiovascular parameters of WKY, SD, and SHR during AW, QS, and PS are compared in Fig. 2. ANOVA detected significant effects of animal group and sleep-wake state on all MAP, RR, HF, LF, LF/HF, HF%, and LF% (P < 0.05) but detected a significant animal group by sleep-wake state interaction on only LF/HF, HF%, and LF% (P < 0.05). MAP, TP, LF, LF/HF, and LF% decreased significantly during QS compared with AW in all WKY, SD, and SHR. The decreases were partially reversed during PS. RR, HF, and HF%, however, increased significantly during QS. RR and HF, especially in WKY, further increased during PS. Apart from that, we found some differences of cardiovascular functions among WKY, SD, and SHR (Fig. 2). WKY and SD had similar MAP, which was significantly lower than that of SHR during all AW, QS, and PS. Compared with WKY and SD, SHR had lower TP, HF, and LF but similar RR, LF/HF, HF%, and LF% during AW. During QS, SHR developed higher LF/HF and LF% in addition to lower HF. SHR ultimately exhibited significantly lower RR accompanied with higher LF/HF and LF% and lower HF during PS compared with WKY. It is noteworthy that LF/HF and LF% of SHR were similar to those of WKY and SD during AW, but they became significantly higher during sleep, especially during QS (Fig. 2).



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  		Fig. 1. Continuous and simultaneous analysis of polysomnogram and heart rate variability (HRV) during active waking (AW), quiet sleep (QS), and paradoxical sleep (PS) in one Wistar-Kyoto rat (WKY; A) and one spontaneously hypertensive rat (SHR; B). Electroencephalogram (EEG) and three-dimensional power spectrogram showing successive power spectral density of the EEG (EPSD) are displayed. Also shown are temporal alterations in the integrated values for the delta wave power of the EEG spectra (delta). The electromyogram power (EMG), R-R intervals (RR), corresponding three-dimensional power spectrogram of RR (HPSD), and quantified values of total power (TP), high-frequency power (HF), low-frequency power (LF)-to-HF ratio (LF/HF), and normalized LF [LF%; in normalized units (nu)] of the spectra are likewise monitored.

 


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  		Fig. 2. Change in mean of RR (RR), TP, HF, LF, LF/HF, normalized HF (HF%), and LF% of HRV and mean arterial pressure (MAP) during AW, QS, and PS in WKY, Sprague-Dawley rats (SD), and SHR. Values are presented as means ± SE; n = 10 rats/group. *P < 0.05 vs. WKY; {ddagger}P < 0.05 vs. SD; {dagger}P < 0.05 vs. AW; #P < 0.05 vs. QS by Fisher's least-significant difference test.

 

Linear regression analyses of both WKY and SHR revealed that the values of LF/HF and LF% were negatively correlated with the magnitude of the delta wave power during QS (Fig. 3). Their correlation coefficients significantly differed from zero (Table 1). These correlations, however, collapsed in AW and PS. We also found that the correlation coefficients of SHR were significantly lower than those of WKY (Table 1). In contrast, HF did not significantly correlate with delta wave power in either group of rats (Fig. 3). The correlation coefficient could not be discriminated from zero for AW, QS, or PS (Table 1).



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  		Fig. 3. Two-dimensional scattergram showing the relation between delta wave power of EEG (delta power) and corresponding HF, LF/HF, and LF% of HRV of one WKY (A) and one SHR (B) during QS.

 

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  		Table 1. Correlation among heart rate variability indexes with EEG delta wave power during AW, QS, and PS in WKY and SHR

 


   DISCUSSION
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
With the application of HRV analysis, our data indicated that although RR, LF/HF and LF% of SHR were similar to those of WKY and SD during AW, SHR developed lower RR or higher LF/HF and LF% during sleep. Meanwhile, HF was lower in SHR than in WKY during all AW, QS, and PS. We concluded that a significant cardiac sympathovagal imbalance with an increased sympathetic modulation occurred in SHR during sleep, although it was less evident during waking. Our study indicated that a classification of sleep-wake states using EEG and EMG may help to detect the autonomic imbalance associated with spontaneous hypertension in rats. Such an observation also implies that a study during sleep may provide a more sensitive way to detect the changes in autonomic functions accompanied with some pathological conditions.

Frequency-domain analysis of HRV provides an opportunity to quantify ANS functions noninvasively. The standards of measurement, physiological interpretations, and clinical uses of HRV were reported in 1996 (26). HF indicates vagal modulations on the cardiac pacemaker, whereas LF/HF reflects cardiac sympathetic modulations or sympathovagal balances (26). The normalized index LF% is suggested to be a quantitative marker of sympathetic modulations (14, 26). The other parameters, such as RR, TP, and LF, are contributed by both cardiac vagal and sympathetic influences (3, 26). In contrast to many well-known ANS function tests, spectral analysis of HRV produces less disturbance and stress to study humans or animals. Such characteristics are especially suitable for sleep studies. In a previous investigation (11), our laboratory developed a computer algorithm for automatic HRV analysis to study the effects of gender and aging on HRV in a large population. This algorithm was modified to achieve an automatic, continuous, and multisignal analysis to meet the needs of sleep studies. With the use of such techniques, our previous studies demonstrated that QS was accompanied by an intense vagal function but an attenuated sympathetic function compared with AW (28, 29), and these findings were compatible with earlier reports (1, 30). The changes of sleep-related autonomic functions associated with spontaneous hypertension, however, have not yet been systemically investigated.

Evidence has suggested that hypertensive subjects have higher sympathetic activity in waking and freely moving humans and animals. On the other hand, some evidence contradicts that. For example, although spontaneous hypertension has been associated with higher sympathetic nerve activity (25), blood pressure variability (4, 14, 18), and cardiac sympathetic modulation (6, 14), indifferent cardiac sympathetic tone (18), similar blood pressure variability (25), and even lower cardiac sympathetic modulation (5) have also been reported. On the basis of analysis of sleep-related autonomic functions, our data revealed that in SHR, LF% and LF/HF, which indicates cardiac sympathetic modulations or sympathovagal balances, were similar to those of WKY during AW. A significant sympathovagal imbalance toward the sympathetic limb, however, appeared in SHR during QS and PS. The difference in sympathetic representation between waking and sleep are noteworthy. A previous study (12) from our laboratory demonstrated that the dynamic pressor control from the rostral ventrolateral medulla in SHR was three times greater than that in WKY. Another recent study (20) also demonstrated that catecholamine-related gene expression in SHR was also three times greater than that in WKY. The large elevation of sympathetic function was not evident in our AW animals. One possible reason is that the complicated neural network in the brain dealing with the huge external information may mask the preset internal mechanism during waking. Thus the strong sympathetic function was not revealed by the sympathetic indexes such as LF/HF and LF% or even by RR during the waking state. When such masking effect was suppressed during sleep, the preset elevation of sympathetic function can be expressed.

Using short period recordings, Friberg et al. (4) estimated the standard deviation of HRV and found that in SHR the cardiac vagal activity was impaired. However, with similar techniques using longer period recordings (24 h), Benessiano et al. (2) demonstrated almost the opposite results. Although the standard deviation of HRV was mainly influenced by the vagal nerve, it is now being realized that the sympathetic function may also play a role in that (3, 26). Discriminating the sympathetic and vagal modulations using spectral analysis, the present study demonstrated that in SHR the HF component of HRV was significantly lower during all sleep-wake states compared with WKY. The values of TP and LF also revealed similar suppression in SHR during all stages. All of these results supported a lower cardiac vagal modulation in SHR. Distinct evidence about the differences in ANS functions between SHR and WKY has been reported under anesthetic conditions (8, 12, 17, 23). It is well appreciated that different anesthetic agents may have diverse effects on the general physiological state, especially autonomic nervous functions (27). Under AW, previous studies (13, 15) have revealed that SHR have sympathetic hyperreactivity to stressful stimuli. The data under sleep, however, have been relatively less reported and may be different from those under anesthesia or AW. It has been reported that sleep triggers a series of changes in autonomic functions (1, 28–30), and the changes may be very vigorous (28, 29). Because most of the previous studies regarding the ANS have been carried out for a relatively short time during the daytime, which is the time when rats sleep, it is in fact difficult to quantify the autonomic changes accurately without a classification of sleep-wake states. On the other hand, discriminating ANS activities using sleep staging and prolonging the recording time may help the accuracy and stability of data analysis (Fig. 4). On the basis of simultaneous and continuous power spectral analysis of EEG, EMG, and HRV signals during 5-h recordings, our computer algorithm divided the changes of ANS functions into different sleep stages, and ANS functions between SHR and WKY can be compared for each stage of the sleep-wake cycle. Our studies were based on simultaneous power spectral analyses of EEG, EMG, and HRV signals, using a fixed 64-s time window length to provide an ideal synchronization of the three signals. Therefore, we were able to perform the correlation analysis between EEG delta wave power and HRV indexes to provide an alternative insight into the interplay between the ANS and cerebral cortex. Under such conditions, our previous studies demonstrated that LF/HF was negatively dependent on the delta wave power, whereas HF was independent of it in normotensive humans (28) and rats (29). Interestingly, the present study revealed that SHR had a similar pattern but with a lower correlation coefficient between LF/HF (or LF%) and delta wave power. The reduced correlation of SHR revealed a decreased coupling between cortical and autonomic functions and may be related to the significant sympathovagal imbalance of SHR during sleep. Whether it is indicative of a poorer sleep quality or a sleep-related disorder warrants further exploration.



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  		Fig. 4. Reproducibility of HRV in one WKY and one SHR. Four complete recordings of electrophysiological signals were made for each of the two rats at the specified day of electrode implantation. HF and LF/HF of HRV during AW (A), QS (B), and PS (C) were separately plotted over time.

 

Apart from a cyclic change of EEG spectrum during sleep, a similar cyclic change of RR spectrogram was also obvious in both SHR and WKY (Fig. 1). During QS as delta wave power of EEG augmented, synchronized HF components of the RR spectra occurred. The synchronization of HF was disrupted by a transition to AW or PS. During PS, however, RR and HF were even larger (see Fig. 1, darker in RR spectrogram) than that during AW and QS. These phenomena were especially obvious in WKY. Traditionally, we use EEG and EMG signals to discriminate different stages of sleep. Beyond our expectations, it was interesting to note that the RR spectrogram itself may also be an indication of sleep conditions.

Monitoring of ANS functions during sleep represents a unique aspect of cardiovascular research. It offers an opportunity to study autonomic dynamics in the face of an intrinsic body environment that is isolated, at least in part, from the extrinsic environment. Thus it is reasonable that some autonomic changes accompanying specific diseases such as hypertension may be highlighted in the sleep state even though they are not very evident in the waking state. The imbalance of cardiac autonomic functions toward the sympathetic limb during sleep in SHR is also compatible with previous findings showing that SHR have a higher sympathetic function but an attenuated vagal function. Although essential hypertension may be caused by multiple factors, the present study suggests that the neural mechanism, especially the sleep-related sympathovagal imbalance, may be an important consideration underlying this prevalent disease.


   ACKNOWLEDGMENTS
 
We thank S. T. Liu and S. F. Kuo for excellent technical support.

GRANTS

This study was supported by National Science Council (Taiwan) Grant NSC-90-2745-P-320-001 and Veteran General Hospital-Taipei Grant VGH-92-371-5.


   FOOTNOTES
 

Address for reprint requests and other correspondence: C. C. H. Yang, Dept. of Physiology, Tzu Chi Univ., 701, Sect. 3, Chung Yang Rd., Hualien 970, Taiwan (E-mail: cchyang{at}mail.tcu.edu.tw).

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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C. J. Lai, C. C. H. Yang, Y. Y. Hsu, Y. N. Lin, and T. B. J. Kuo
Enhanced sympathetic outflow and decreased baroreflex sensitivity are associated with intermittent hypoxia-induced systemic hypertension in conscious rats
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