Abstract

Heart rate variability is a relevant predictor of cardiovascular risk in humans. However, to use heart and blood pressure (BP) variability or baroreflex sensitivity as markers for hypertensive pregnancy disorders, it is first necessary to describe these parameters in normal pregnancy. To accommodate the complexities of autonomic cardiovascular control we added parameter domains of nonlinear dynamics to conventional linear methods of time and frequency domains. The BP of 27 women with normal pregnancy and 14 nonpregnant women were monitored at a high resolution (200 Hz sampling frequency) using a Portapres for 30 min. The pregnant women were divided into groups of 32 or less or greater than 32 weeks of gestation. Pregnant and nonpregnant women were classified into subclasses of maternal age of less than 28 or 28 or more years.

Except for two single parameter domains, we found no significant differences in heart rate and BP variability for pregnant women with different gestational age or different maternal age. Moreover, no significant differences in spontaneous baroreflex sensitivity could be found between pregnant women regardless of either their age or gestational age. In contrast, all measures of nonlinear dynamics of heart rate variability as well as all parameter domains of spontaneous baroreflex sensitivity showed significant changes between pregnant and nonpregnant women, whereas BP variability did not differ between those groups. This complex assessment of autonomic cardiovascular regulation has shown that the parameters tested are stable in the second half of normal pregnancy, and might have the potential to be excellent indicators of pathophysiologic conditions.

Blood pressure variability (BPV) and heart rate variability (HRV) are generated by the rhythmic actions of cardiovascular hormones and neuronal pathways on effector organs such as the heart, kidneys, and vessels. Heart rate variability has been shown to be a relevant predictor for the mortality of patients with myocardial infarction. The clinical importance of HRV has been confirmed in several studies since the late 1980s. HRV is a strong and independent predictor of mortality after an acute myocardial infarction,1,,,,,7 but even age and gender can influence blood pressure (BP) and heart rate, and lead to variability changes.8,,,12 Similarly, the sinoaortic baroreflex sensitivity (BRS), which is one of the primary mechanisms that regulates BP, is correlated to the incidence of pathophysiologic conditions. For instance, numerous studies have reported baroreflex resetting with aging and after long-term exposure to chronic hypertension.13,14

The brain seems to be the source for most of the signals thought to be responsible for the setting of BP and HR.15 The molecular mechanisms involved in the regulation of BPV and HRV or BRS are only poorly understood, although initial analysis of gene-manipulated animals had shown that genes involved in cardiovascular regulation and expressed in the central nervous system should be interesting candidates for the molecular regulation of BPV and HRV.16,,19

Although significant adaptations of the maternal cardiovascular system are known to occur during normal pregnancy, our specific knowledge of the maternal HRV and BPV during pregnancy is poor. However, it has been reported that BPV20,21 and HRV22,23 are decreased in normal pregnancy. In addition, Eneroth and Storck24 described an unchanged HRV in women with preeclampsia and women hospitalized due to different complications compared to healthy pregnant women, whereas other groups described differences in cardiovascular variabilities in preeclamptic pregnancies.22,25,,28

This study was performed to determine whether the parameter domains of HRV, BPV, or BRS in normal pregnancy are influenced by either the gestational age or by the age of the pregnant women. To investigate the complex modulation of the autonomic system we introduced new methods of nonlinear dynamics and combined these with traditional methods and an advanced technology of BRS analysis by the Dual Sequence Method.29

Methods

Subjects

In this cross-sectional study, 27 healthy pregnant women (age: 28.9 ± 5.5 years; gestational week: 32 ± 6; range: 21st to 40th week of gestation) were recruited from the Department of Obstetrics, University of Leipzig, from May to December 1998. Informed consent was obtained from all patients. All women had single pregnancies, normal BP during the pregnancy, and normal pregnancy outcome. Women with diabetes, or cardiovascular and renal diseases were excluded. None of the women had a history of hypertensive disorders in a previous pregnancy. Betamimetics or other drugs with cardiovascular effects were not given to the women. As a control group, 14 age-matched healthy women (age: 27.9 ± 5.6 years) were investigated. None of these controls had a cardiovascular or renal disease or took drugs with cardiovascular effects.

Data acquisition and preprocessing

The beat-to-beat BP values were recorded under standardized resting conditions for all pregnant women and the controls using the Portapres device (portable, volume-clamp photoplethysmographic BP device, Model 2; BMI-TNO, Amsterdam, The Netherlands), a 30 min recording time (to have a clinical relevant and feasible time of monitoring), and a 200 Hz sampling frequency. Simultaneously, a Holter ECG (Avionics, Leesburg, Virginia) with a resolution of 8 ms was recorded. The women were lying comfortably on their left side with an approximately 30° tilt towards the observer. To exclude an influence of the period of the day, all values were recorded between 8 AM and 12 AM.

The recorded data were filtered to exclude ventricular premature beats, artifacts, and noise. Furthermore, the time series were filtered using a moving average filter (2nd order) before calculating the BRS, to smooth and amplify the dominant signal components. Standard frequency domain parameters of HRV and BPV presuppose an equidistant sampled time series. Neither the beat-to-beat interval (BBI) time series (sequence of successive beat-to-beat differences) nor the BP time series are equidistant in terms of time, but they are in terms of the actual beat number. It was therefore necessary to create equidistant time series by interpolation (Δt = 500 ms), to get spectra with frequency scaling. The normal and edited BBI after filtering are denoted as NN intervals for HRV and BPV.

Analysis of HRV

From the time domain the following parameters25,26 were calculated:

In the frequency domain we used these frequency bands:

The spectra were estimated using the Fast Fourier Transform. To avoid the leakage effect a Blackman Harris window function was applied. Furthermore, the measures of normalized LFn and UVLF as the sum of ULF + VLF + LF were calculated.

Symbolic dynamics, as a nonlinear approach to investigate complex systems, facilitates the analysis of dynamic aspects of heart rate and BP variability. The concept of symbolic dynamics is based on a coarse-graining of the dynamics.27,28,30,31 The Renyi entropy calculated from these distributions of words (FWRen025 − a = 0.25) is a suitable measure for the complexity within the time series. Higher values of this entropy refer to a higher complexity within the corresponding tachograms and lower values to lower complexity. A high percentage of words consisting only of the symbols ‘0’ and ‘2’ (WPSUM02) is a measure for a decreased HRV. Forbidden words (FW) are those words that seldom or never occur (a probability less than .001) within the distribution of words with a length of 3.

Additionally, a second mode of symbolic dynamics for high-variability analysis was used in this study. In this case we observed six successive symbols of a simplified alphabet {0,1}. Here, the symbol ‘0’ stands for a difference between two successive beats lower than a special limit of 20 ms, whereas ‘1’ represents those cases where the difference between two successive beats exceeds this special limit. Words consisting only of unique type of symbols (all ‘1’) were counted. This measure is called PHVAR20 and quantifies an increased variability.

Analysis of BPV

From the time domain the following parameters were calculated:

In the frequency domain these frequency bands were used:

The measures of normalized bLFn and bUVLF (sum of bULF + bVLF + bLF) were calculated.

Estimation of baroreflex sensitivity

The parameter domains with the most relevance to estimation of the spontaneous baroreflex are the slopes as a measure of the sensitivity.32,33 The slopes were usually calculated by linear regression BRS = ΔBBI/ΔBP (ms/mm Hg) with BP as systolic BP. Using the Dual Sequence Method29 two kinds of BBI responses were analyzed (with different synchronization modes and splitted sectors): bradycardiac fluctuations (an increase in BP causes an increase in BBI) and tachycardic fluctuations (a decrease in BP causes a decrease in BBI). The analysis of the tachycardic and bradycardic fluctuations contains three consecutive BP and BBI values.

The following parameters were calculated:

Statistics

The two-tailed univariate t test for equality of means was used to test whether the parameter means of the various groups differed significantly from each other. The analysis includes seven different tests:

Additionally, multivariate analysis of variance (MANOVA) with all parameter domains was performed to test whether age or pregnancy have a significant effect on the population means of the parameter domains considered. Afterwards, one-factorial MANOVA tests were performed to prove whether the gestational age has a significant effect on the population means. Kolmogorov-Smirnov test and Shapiro-Wilk test were used to prove normal distribution within the groups.

Results

(Fig. 1) and (Fig. 2) show examples of the HRV and BPV of a healthy pregnant woman in comparison to the variabilities of a nonpregnant healthy woman.

Systolic blood pressure (SBP) and beat-to-beat intervals (RR) of a pregnant healthy woman.

Systolic blood pressure (SBP) and beat-to-beat intervals (RR) of a nonpregnant healthy woman.

We found no significant differences in HRV, BPV, and BRS associated with maternal age (Test 1: mean age difference, 9 years) and parity (data not shown). Also the respiratory frequency was unchanged. A similar result was obtained within the nonpregnant controls (Test 2) with one exception: the LFn of HRV was significantly reduced with increasing age (Table 1); however, MANOVA analysis using all variable sets of all parameter domains showed no significant effect of age on all parameter domains.

Table 1.

Results presented as mean ± standard deviation of test 1 (differences caused by maternal age), test 2 (differences caused by age of the nonpregnant controls), test 3 (differences between pregnant and nonpregnant < 28 years), and test 4 (differences between pregnant and nonpregnant ≥ 28 years)

 P<28 NP<28 P≥28 NP≥28 Test 1 P Test 2 P Test 3 P Test 4 P 
 Mean SD Mean SD Mean SD Mean SD     
HRV             
meanNN 695 89 805 103 771 139 856 60 NS NS .023 NS 
SDNN 42 11 70 21 45 17 67 24 NS NS .002 NS 
RMSSD 25 10 51 21 27 51 22 NS NS .004 .007 
Renyi4 1.8 0.2 2.3 0.3 1.8 0.4 2.2 0.4 NS NS .000 NS 
ULF 0.07 0.06 0.09 0.05 0.15 0.24 0.24 0.22 NS NS NS NS 
VLF 0.23 0.14 0.56 0.58 0.19 0.13 0.34 0.19 NS NS NS NS 
LF 0.15 0.11 0.46 0.2 0.09 0.05 0.33 0.32 NS NS .001 0.037 
HF 0.07 0.06 0.27 0.21 0.07 0.04 0.29 0.22 NS NS .015 0.007 
LFn 0.66 0.12 0.66 0.08 0.6 0.13 0.53 0.14 NS .045 NS NS 
FW 24 25 12 10 NS NS .001 .008 
FWRen025 3.7 0.2 3.9 0.1 3.6 0.2 3.9 0.1 NS NS .003 .005 
phvar20 0.008 0.013 0.068 0.081 0.006 0.007 0.079 0.086 NS NS .034 .016 
WPSUM02 0.44 0.14 0.21 0.1 0.48 0.17 0.25 0.18 NS NS .001 .029 
BPV             
bmeanNN 104 15 114 11 110 14 113 NS NS NS NS 
bsDNN NS NS NS NS 
bULF 0.006 0.008 0.004 0.003 0.003 0.003 0.008 0.006 NS NS NS NS 
bVLF 0.011 0.008 0.007 0.002 0.007 0.006 0.006 0.003 NS NS NS NS 
bUVLF 0.017 0.015 0.011 0.003 0.01 0.008 0.014 0.009 NS NS NS NS 
bLFn 0.86 0.08 0.75 0.16 0.78 0.17 0.79 0.06 NS NS NS NS 
BRS             
all_tachy 11 17 13 21 NS NS .003 .003 
all_>5_tachy 13 19 14 22 NS NS .002 .004 
all_brady 10 19 12 21 NS NS .001 .005 
all_>5_brady 12 20 13 22 NS NS .001 .005 
 P<28 NP<28 P≥28 NP≥28 Test 1 P Test 2 P Test 3 P Test 4 P 
 Mean SD Mean SD Mean SD Mean SD     
HRV             
meanNN 695 89 805 103 771 139 856 60 NS NS .023 NS 
SDNN 42 11 70 21 45 17 67 24 NS NS .002 NS 
RMSSD 25 10 51 21 27 51 22 NS NS .004 .007 
Renyi4 1.8 0.2 2.3 0.3 1.8 0.4 2.2 0.4 NS NS .000 NS 
ULF 0.07 0.06 0.09 0.05 0.15 0.24 0.24 0.22 NS NS NS NS 
VLF 0.23 0.14 0.56 0.58 0.19 0.13 0.34 0.19 NS NS NS NS 
LF 0.15 0.11 0.46 0.2 0.09 0.05 0.33 0.32 NS NS .001 0.037 
HF 0.07 0.06 0.27 0.21 0.07 0.04 0.29 0.22 NS NS .015 0.007 
LFn 0.66 0.12 0.66 0.08 0.6 0.13 0.53 0.14 NS .045 NS NS 
FW 24 25 12 10 NS NS .001 .008 
FWRen025 3.7 0.2 3.9 0.1 3.6 0.2 3.9 0.1 NS NS .003 .005 
phvar20 0.008 0.013 0.068 0.081 0.006 0.007 0.079 0.086 NS NS .034 .016 
WPSUM02 0.44 0.14 0.21 0.1 0.48 0.17 0.25 0.18 NS NS .001 .029 
BPV             
bmeanNN 104 15 114 11 110 14 113 NS NS NS NS 
bsDNN NS NS NS NS 
bULF 0.006 0.008 0.004 0.003 0.003 0.003 0.008 0.006 NS NS NS NS 
bVLF 0.011 0.008 0.007 0.002 0.007 0.006 0.006 0.003 NS NS NS NS 
bUVLF 0.017 0.015 0.011 0.003 0.01 0.008 0.014 0.009 NS NS NS NS 
bLFn 0.86 0.08 0.75 0.16 0.78 0.17 0.79 0.06 NS NS NS NS 
BRS             
all_tachy 11 17 13 21 NS NS .003 .003 
all_>5_tachy 13 19 14 22 NS NS .002 .004 
all_brady 10 19 12 21 NS NS .001 .005 
all_>5_brady 12 20 13 22 NS NS .001 .005 

P = pregnant women; NP = nonpregnant controls; Pvalue = level of significance; HRV = heart rate variability; NS = not significant; for other abbreviations, see the Methods section.

Advanced gestational age influenced only one frequency-domain parameter of HRV (Test 5). A higher gestational age is correlated with an increase of the power within the ULF frequency band. Parameter domains of BPV remain stable with advancing gestational age, except for a decrease of the mean value of systolic BP from 113 to 100 mg Hg. Also the parameter domains of BRS are unchanged after the 32nd week of gestation (Test 5). Multivariate MANOVA excluded a significant dependency of the gestational age on all investigated parameter domains, including mean systolic BP (bmeanNN, P < .64).

In comparing pregnant to nonpregnant women with an age < 28 or ≥ 28 years (Tests 3 and 4), there were considerable differences in HRV and BRS. In contrast, for BPV no significant differences could be found between the groups. In test 3, all four time-domain parameters of HRV were decreased in pregnant women, as were two of the four frequency-domain parameters of HRV. All four parameter domains of nonlinear dynamics showed significant changes, too. In Test 4 only one time-domain parameter of HRV (RMSSD), but the same two frequency-domain parameters of HRV as in Test 3, were significantly decreased. Finally, we calculated high significance for all four parameter domains of BRS in Tests 3 and 4 (Table 1).

The comparison of pregnant women ≤ 32 weeks to nonpregnant controls (Test 6) showed a significant decline in all parameter domains of the HRV, except LFn, and all parameter domains of BRS, except an unchanged BPV. Compared to Test 6, the group with gestational age > 32 weeks was characterized by a smaller number of significantly altered parameter domains of HRV, compared to the nonpregnant controls (Test 7). However, in both tests all parameter domains of BRS were significantly decreased (Table 2).

Table 2.

Results presented as mean ± standard deviation of test 5 (differences caused by gestational age in weeks), test 6 (differences between pregnant women with a gestational age ≤ 32 weeks and nonpregnant controls), and test 7 (differences between pregnant women with a gestational age > 32 weeks and nonpregnant controls)

 ≤ 32 weeks > 32 weeks NP Test 5 P Test 6 P Test 7 P 
 Mean SD Mean SD Mean SD    
HRV          
meanNN 709 91 770 124 843 70 NS .001 NS 
SDNN 43 10 46 18 68 18 NS .000 .007 
RMSSD 26 25 50 16 NS .000 .000 
Renyi4 1.8 0.2 1.8 0.4 2.3 0.3 NS .000 .006 
ULF 0.06 0.04 0.2 0.22 0.16 0.16 .036 .033 NS 
VLF 0.2 0.11 0.24 0.16 0.38 0.17 NS .006 NS 
LF 0.11 0.08 0.13 0.11 0.41 0.25 NS .001 .003 
HF 0.07 0.04 0.07 0.07 0.25 0.15 NS .001 .002 
LFn 0.64 0.14 0.64 0.1 0.61 0.13 NS NS NS 
FW 24 27 9.4 7.6 NS .000 .000 
FWRen025 3.6 0.2 3.6 0.2 3.9 0.1 NS .000 .000 
phvar20 0.008 0.013 0.007 0.011 0.065 0.062 NS .005 .006 
WPSUM02 0.45 0.12 0.49 0.21 0.23 0.12 NS .000 .002 
BPV          
bmeanNN 113 14 100 111 .02 NS .013 
bsDNN NS NS NS 
bULF 0.004 0.004 0.004 0.003 0.006 0.005 NS NS NS 
bVLF 0.008 0.007 0.008 0.006 0.006 0.003 NS NS NS 
bUVLF 0.012 0.01 0.012 0.009 0.012 0.006 NS NS NS 
bLFn 0.82 0.15 0.76 0.16 0.81 0.06 NS NS NS 
BRS          
all_tachy 13 11 19 NS .001 .000 
all_>5_tachy 14 12 20 NS .001 .000 
all_brady 11 10 20 NS .000 .000 
all_>5_brady 12 12 21 NS .000 .000 
 ≤ 32 weeks > 32 weeks NP Test 5 P Test 6 P Test 7 P 
 Mean SD Mean SD Mean SD    
HRV          
meanNN 709 91 770 124 843 70 NS .001 NS 
SDNN 43 10 46 18 68 18 NS .000 .007 
RMSSD 26 25 50 16 NS .000 .000 
Renyi4 1.8 0.2 1.8 0.4 2.3 0.3 NS .000 .006 
ULF 0.06 0.04 0.2 0.22 0.16 0.16 .036 .033 NS 
VLF 0.2 0.11 0.24 0.16 0.38 0.17 NS .006 NS 
LF 0.11 0.08 0.13 0.11 0.41 0.25 NS .001 .003 
HF 0.07 0.04 0.07 0.07 0.25 0.15 NS .001 .002 
LFn 0.64 0.14 0.64 0.1 0.61 0.13 NS NS NS 
FW 24 27 9.4 7.6 NS .000 .000 
FWRen025 3.6 0.2 3.6 0.2 3.9 0.1 NS .000 .000 
phvar20 0.008 0.013 0.007 0.011 0.065 0.062 NS .005 .006 
WPSUM02 0.45 0.12 0.49 0.21 0.23 0.12 NS .000 .002 
BPV          
bmeanNN 113 14 100 111 .02 NS .013 
bsDNN NS NS NS 
bULF 0.004 0.004 0.004 0.003 0.006 0.005 NS NS NS 
bVLF 0.008 0.007 0.008 0.006 0.006 0.003 NS NS NS 
bUVLF 0.012 0.01 0.012 0.009 0.012 0.006 NS NS NS 
bLFn 0.82 0.15 0.76 0.16 0.81 0.06 NS NS NS 
BRS          
all_tachy 13 11 19 NS .001 .000 
all_>5_tachy 14 12 20 NS .001 .000 
all_brady 11 10 20 NS .000 .000 
all_>5_brady 12 12 21 NS .000 .000 

Abbreviations as in Table 1.

Discussion

The analysis of heart rate and BP variability is a noninvasive diagnostic tool that provides important prognostic information concerning individual risk, for instance, after acute myocardial infarction.3,,,7 In contrast to previous investigations on HRV and BPV in pregnant women,20,,,24 our study provides a more complex and multiparametric approach, including traditional methods of HRV and BPV; time domain measures; frequency domain measures combined with new methods of nonlinear dynamics;26,30 and BRS analysis.34

Thus, we analyzed the cardiovascular parameters of 27 pregnant women to determine whether the gestational age or the age of the pregnant women influence parameter domains of HRV, BPV, or BRS in normal pregnancy. With the exception of two parameter domains of HRV (LFn and ULF), there were no significant differences. Investigation on the influence of biologic age on HRV have shown that the alterations in these parameters can occur as long-term effects over decades.8,10,12 For this reason, significant changes due to aging were not to be expected in our study, mainly because of the relatively short time frame observed, with a mean age difference of 9 years in the pregnant women and 10 years in the nonpregnant controls, respectively. On the other hand, the decrease of LFn with maternal age might be caused by an aging effect, as LF power declines linearly across the whole age range.30

Increased power within the ULF frequency band of the HRV depending on the gestational age can result from several reasons. A number of investigators have shown that systems like the renin–angiotensin system or the thermoregulation system influence the ULF parameter domain.34 However, instabilities caused by physical or mental stress can specifically alter the ULF band. Possibly, the fetal movements could trigger maternal reactions, inducing changes in this frequency band.

In addition, the systolic BP was significantly decreased in the group over 32 weeks of gestation, but all linear and nonlinear parameter domains of BPV were unchanged. These data are in contrast to those of Ayala and Hermida et al,20,21 who showed decreased parameters of BPV as well as an increase in systolic BP from the 21st week until delivery, and are contrary to routine clinical observations. The most likely explanation for this observation is our methodical approach, which uses continuous finger arterial pressure recording. Furthermore, all parameters of BRS remain stable with advancing gestational age. In addition, the data of 14 healthy nonpregnant women were acquired to investigate differences between pregnant and nonpregnant women. During pregnancy significant adaptive changes of the cardiovascular system occur. In previous studies a decreased HRV with a reduced high-frequency power in the second trimester of normal pregnancy compared to nonpregnant women has been described.22,35 Our data support these findings, with a reduced power in the HF (0.15–0.4 Hz) and in the LF (0.04–0.15 Hz). In addition, we were able to show that these alterations are also detectable in the third trimester, which implicates a reduced vagally mediated activity in the second half of pregnancy. Furthermore, our data show significantly decreased measures of nonlinear dynamics such as FW, FWRen025, phvar20, and WPSUM02. These methods of nonlinear dynamics describe complex fluctuations in rhythm and separate structures of nonlinear behavior in the heart rate time series more successfully than classical methods of time and frequency domains do.26,30 This provides additional support for the described decrease in HRV in normal pregnant women. Moreover, the differences in the HRV between pregnant and nonpregnant women seem to be more drastic in the second trimester than in the third trimester. This corresponds to the known clinical finding that the highest increase of heart rate and volume load occur until the end of the second trimester in normal pregnancy. The physiologic reduction in HRV may result not only from an increased resting heart rate, but also from the state of chronic volume overload, with an increased preload in pregnancy that is also highest at the end of the second trimester. Thus, physiologic cardiovascular changes may lead to an impaired adaptive capacity. Why this impairment, which is considered to be of negative prognostic value in patients with a cardiovascular disease,3,,,7 is observable in healthy pregnant women remains an open question. Interestingly, none of the time and frequency domains of BPV differed between the pregnant and nonpregnant women, which is in contrast to previous studies.20,21

Using first the Dual Sequence Method29 to analyze the BRS in pregnancy, we have shown that tachycardic as well as bradycardic fluctuations are decreased in pregnant women. Moreover, the interpretation of changes in spontaneous BRS may explain changes in vagal HR modulation due to the predominant role of the parasympathetic system and HR regulation, within the time constants explored by these methods. Secondly, we interpret this decreased BRS as a parallel effect to the decreased HRV, as both quantify the effects of autonomic cardiac modulation. In addition, we can exclude an influence of the respiratory frequency on all investigated parameters, because no differences could be detected between pregnant and nonpregnant women, as was also shown previously.36

With respect to the feasibility of the method for clinical use, recording time was confined to 30 min. However, data of slower BP and HR fluctuations cannot be derived from a 30-min recording. With our more complex approach combining linear measures of HRV, BPV, and BRS with the investigation of inner motions of the time series using symbolic dynamics we have been able to provide an improved description of the autonomic cardiovascular control in pregnancy. These nonlinear parameters record the interactions of the regulation system with a higher complexity. Thus, in the second half of normal pregnancy the parameter domains of HRV, BPV, and BRS show a high degree of stability related to maternal and gestational age. Therefore, we conclude that this method could be a useful tool to detect pathophysiologic changes, particularly in hypertensive pregnancy disorders.

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