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Am J Physiol Heart Circ Physiol 280: H1400-H1406, 2001;
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Vol. 280, Issue 3, H1400-H1406, March 2001

Determinants and interindividual variation of R-R interval dynamics in healthy middle-aged subjects

Sirkku M. Pikkujämsä, Timo H. Mäkikallio, K. E. Juhani Airaksinen, and Heikki V. Huikuri

Division of Cardiology, Department of Medicine, University of Oulu, 90020 Oulu, Finland


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

Determinants and intersubject variations of fractal and complexity measures of R-R interval variability were studied in a random population of 200 healthy middle-aged women (age 51 ± 6 yr) and 189 men (age 50 ± 6 yr) during controlled conditions in the supine and sitting positions. The short-term fractal exponent (alpha 1) was lower in women than men in both the supine (1.18 ± 0.20 vs. 1.12 ± 0.17, P < 0.01) and sitting position (P < 0.001). Approximate entropy (ApEn), a measure of complexity, was higher in women in the sitting position (1.16 ± 0.17 vs. 1.07 ± 0.19, P < 0.001), but no gender-related differences were observed in ApEn in the supine position. Fractal and complexity measures were not related to any other demographic, laboratory, or lifestyle factors. Intersubject variations in a fractal measure, alpha 1 (e.g., 1.15 ± 0.20 in the supine position, z value 1.24, not significant), and in a complexity measure, ApEn (e.g., 1.14 ± 0.18 in the supine position, z value 1.44, not significant), were generally smaller and more normally distributed than the variations in the traditional measures of heart rate variability (e.g., standard deviation of R-R intervals 49 ± 21 ms in the supine position, z value 2.53, P < 0.001). These results in a large random population sample show that healthy subjects express relatively little interindividual variation in the fractal and complexity measures of heart rate behavior and, unlike the traditional measures of heart rate variability, they are not related to lifestyle, metabolic, or demographic variables. However, subtle gender-related differences are also present in fractal and complexity measures of heart rate behavior.

gender; heart rate variability; nonlinear methods; risk factors


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

TRADITIONAL HEART RATE (HR) variability measures have been found to be related to several clinical, lifestyle, and laboratory factors (12, 20, 24), and gender-related differences in conventional measures of HR variability have also been described (7, 10, 13, 20). Healthy subjects also show marked interindividual variation in the time and frequency domain measures of HR variability (6, 12, 20, 24).

Recently, new dynamic methods of R-R interval variability have been used in conjunction with the traditional measures to uncover subtle but important abnormalities or alterations in time series data that are not otherwise apparent (14). Several articles (4, 5, 16-19) have been published using these fractal analysis methods [detrended fluctuation analysis (DFA)] and complexity methods [approximate entropy (ApEn)] in various cardiovascular disorders. Previous studies have suggested that the complexity and fractal measures of HR dynamics differ between men and women (25, 27) and that, although complexity is decreased during the adult life, the short-term fractal scaling (alpha 1) is not strongly related to age in adults (25). However, there are no large-scale population-based studies on determinants and intersubject variation of dynamic behavior of HR variability in healthy subjects. This study was designed to assess the R-R interval dynamics of healthy middle-aged subjects in standard conditions by short-term fractal measure in alpha 1 and ApEn, a measure of complexity, and to test the relations of measures of R-R interval dynamics to various clinical, lifestyle, and laboratory factors.


    SUBJECTS AND METHODS
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

The cohort used in this study was comprised of 600 subjects (300 men and 300 women) living in the Oulu district (Oulu, Finland). The subjects were randomly selected from the register of the Social Insurance Institution as age- and sex-matched control subjects to hypertensive subjects used in another study (33). To be selected, a subject had to live in the Oulu district but not to be entitled to a refund of antihypertensive medication (thus practically all subjects with long-standing hypertension were excluded). The subjects were 40-59 yr old at the beginning of the study (1990). Four hundred and eighty-eight subjects (81%) completed the whole study protocol. Informed consent was obtained, and the Ethics Committee of the University of Oulu approved the protocol. After excluding subjects with angina pectoris or dyspnea, electrocardiographic (ECG) or echocardiographic evidence of heart disease, cardiovascular medication, diabetes or fasting blood glucose >6.7 mmol/l, elevated blood pressure during ambulatory blood pressure recording (>140/90 mmHg), or technical artifacts or rhythm abnormalities during the ECG recording, further analyses were performed with 189 healthy middle-aged men and 200 women.

The study subjects were interviewed by a physician, and a standardized health questionnaire of past medical history, medication used, cardiac symptoms (angina pectoris or dyspnea), smoking habits, alcohol consumption, physical activity, and personality type was filled in. All subjects went through a clinical examination. A wide range of laboratory tests (including a 2-h glucose tolerance test) and echocardiography were performed. Ambulatory blood pressure was recorded using a fully automatic SpaceLabs 90207 oscillometric unit (SpaceLabs; Redmond, WA) as described previously (33). All methods have been described in detail earlier (7, 24, 33). The clinical data, lifestyle variables, and laboratory values of the populations are listed in Table 1.

                              
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  		Table 1.   Demographic, lifestyle, and laboratory data of the study populations

ECG Recordings and Analysis of R-R Interval Dynamics

The ECG recordings were obtained for 45 min with an ambulatory ECG recorder. First, the subjects were in the supine position for 6 min, the time the ECG recorder needed for calibration. Thereafter, the ECG was recorded for 15 min while the subjects were quietly lying down, 15 min while in the sitting position, and 15 min while walking. In this study, we wanted to study R-R interval dynamics in more standard conditions, however. The middle 13 min of both supine and sitting periods were used in the analysis of this study to avoid the possible confounding effects of nonstationarities and artifacts during the changes in body posture. To verify stationarity of the measurement segments in the supine and sitting positions, the mean R-R interval and standard deviation of R-R intervals (SDNN) were calculated in 1-min intervals, and frequency domain measures in the first and last 5-min periods (Tables 2 and 3) were calculated in the supine and sitting positions in a subgroup of 25 men and 25 women. No significant trends or changes were observed in the average HR or SDNN during the 13-min period, and the frequency domain measures did not differ between the first and last 5-min period. The digitized ECG data were transferred from the scanner to a computer for analysis of HR variability with a custom-made program. The program automatically detects and labels each QRS complex. Premature beats and noise were deleted automatically and manually from the computer-formed tachograms with previously described methods (7). Data with >85% of qualified beats were included in the final analysis.

                              
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  		Table 2.   Stationarity of HR in supine and sitting position


                              
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  		Table 3.   Stationarity of HR variability in supine and sitting position

Traditional time and frequency domain measures. The mean R-R interval and SDNN were used as time domain measures. After linear detrending, an autoregressive model, with the value of 20 as the order of the model, was used to estimate the spectral power (in ms2) in the low-frequency (LF; 0.04-0.15 Hz) and high-frequency (HF; 0.15-0.4 Hz) bands. The spectral components were analyzed as absolute units and also as normalized units. The ratio of LF to HF components was also calculated. Normalized units of LF and HF were calculated by multiplying the power of each spectra by 100 and then by dividing it with the sum of the power of LF and HF spectra (7, 22).

Fractal scaling and complexity measures. The DFA technique was used to quantify the fractal-like scaling properties of R-R interval data. The root-mean-square fluctuations of the integrated and detrended data were measured in observation windows of varying size and then plotted against the size of the window on a log-log scale. The scaling exponent alpha  represents the slope of the line that relates (log) fluctuation to (log) window size (9, 23). In this study, alpha 1 (4-11 beats) was calculated from a period of 13 min.

ApEn is a measure quantifying the regularity or complexity of time series (26). Lower values of ApEn indicate a more regular (less complex) signal; higher values indicate more irregularity (greater complexity). ApEn was calculated with fixed input variables m = 2 and r = 20% with a method described earlier by Pincus and Goldberger (26) from a period of 13 min.

Statistics

Results are mostly given in means ± SD. The statistical analysis of the spectral measures of HR variability and other variables with highly skewed distributions were performed after logarithmic transformation of the absolute values. Associations between measures of R-R interval dynamics and other variables were analyzed with univariate techniques (bivariate correlation and analysis of variance). Comparisons between men and women were performed using the independent samples t-test and between supine and upright position using the paired-samples t-test. Differences in the baseline variables between men and women were taken into account by analysis of covariance (ANCOVA) using clinical and laboratory values as covariates. P < 0.01 was considered statistically significant. Gaussian distribution of the data was analyzed by the Kolmogorov-Smirnov goodness-of-fit test.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

The interindividual variations in the measures of R-R interval dynamics are shown in Table 4. The interindividual variation, expressed as means ± SE, the range, was generally smaller in the fractal and complexity measures than in the traditional time and frequency domain measures. The individual values of fractal scaling exponents and ApEn were also more normally distributed than the SDNN and spectral measures of HR variability (see Fig. 1).

                              
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  		Table 4.   Interindividual variation in measures of R-R interval dynamics in healthy middle-aged subjects



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  		Fig. 1.   Different measures of R-R interval variability in healthy middle-aged men (n = 189) and women (n = 200) in the supine and sitting position. NS, not significant; alpha 1, short-term fractal component; ApEn, approximate entropy; SDANN, standard deviation of R-R intervals.

When the measures of R-R interval dynamics were compared in the upright (sitting) versus supine position, alpha 1, SDNN, and LF spectral power (both in absolute and normalized units) were significantly higher and mean R-R intervals were lower in the upright (sitting) versus supine position (P < 0.001 for all; Table 4). No significant differences were found in ApEn or HF spectral power in absolute units, but the normalized HF power decreased in the sitting position (P < 0.001).

Tables 5 and 6 list the significant bivariate correlations between the measures of R-R interval dynamics measured in the supine position and other variables in men and women. alpha 1, ApEn, LF-to-HF ratio, and normalized LF or HF power were not related to any demographic or laboratory factors. Time and frequency domain measures (in absolute units) had significant inverse relations to age and serum insulin levels in both men and women and additionally to systolic blood pressure and serum triglyceride levels in men. Also, direct relations were found to high-density lipoprotein-cholesterol in men. No significant relations were found with any measure of R-R interval dynamics to current smoking habits, alcohol consumption, leisure time physical activity, personality type, or echocardiographic parameters in either men or women. When the measures of R-R interval dynamics were analyzed in the supine or upright position separately, the relations to above mentioned factors were similar. A stepwise multiple regression analysis, including all variables having significant univariate correlation with each measure of R-R interval dynamics (Tables 5 and 6), was performed with the particular measure of R-R interval dynamics as a dependent variable. The results showed that 8-10% of the interindividual variation of SDNN and 6-10% spectral components in women and men, respectively, were explained by these variables (R2 of the model).

                              
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  		Table 5.   Significant relationships of measures of R-R interval dynamics to demographic and laboratory parameters in healthy middle-aged men in the supine position


                              
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  		Table 6.   Significant relationships of measures of R-R interval dynamics to demographic and laboratory parameters in healthy middle-aged women in the supine position

Women had significantly lower blood pressure, serum lipid values, fasting blood glucose and insulin values, and left ventricular mass compared with men (P < 0.001 for all; Table 1). Women consumed less alcohol (P < 0.001), and the Framingham psychosocial score was higher (P < 0.001) in women. The measures of R-R interval dynamics in men and women are shown in Table 7 and Fig. 1. In the supine position, women had a lower SDNN (P < 0.01), lower normalized HF component (P < 0.01), and lower alpha 1 value (P < 0.01) compared with men. In the sitting position, the gender-related differences were more marked, so that ApEn, normalized LF power, and LF-to-HF ratio also significantly differed between women and men (see Table 7). When observed differences in the baseline variables between men and women were taken into account by ANCOVA using clinical and laboratory variables as covariates, alpha 1 was still lower both in the supine and sitting position (P < 0.01 for both), and ApEn was higher in the sitting position ( P < 0.01).

                              
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  		Table 7.   Measures of R-R interval dynamics in healthy middle-aged men and women


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

Time and frequency domain analyses are the most commonly used noninvasive methods to evaluate autonomic regulation of HR in healthy subjects. Because nonlinear phenomena are involved in the genesis of human HR fluctuations, new analysis techniques have been developed to probe nonlinear features in HR behavior (2, 23, 28, 30, 32) that are not detectable by traditional analysis methods. Among many new methods, the measurement of fractal scaling exponents by DFA is one such method that describes the fractal-like correlation properties of R-R interval data (9, 23). ApEn is another nonlinear method that quantifies the amount of complexity in the time series data (26). Both these methods have been shown to provide clinically important information on the abnormal HR behavior in various cardiovascular disorders (4, 16-19, 23, 31), but no large-scale studies have focused on the determinants and interindividual variation of these indexes in healthy subjects.

The results of this study in a large random population sample show that there are gender-related differences in the fractal and complexity measures of R-R interval dynamics, but they are not substantially related to gender-related differences in cardiovascular risk factors in healthy subjects. The interindividual variation in the dynamical measures of R-R interval variability based on complexity and fractal analysis in healthy subjects was also smaller than that of the traditional time and frequency domain measures of HR variability. Both fractal scaling exponents and ApEn were more normally distributed in this population, whereas the values of SDNN and spectral measures had a more skewed distribution.

Fractal and Complexity Measures of HR Variability

New dynamical methods of R-R interval variability based on fractals and complexity analysis have been used in conjunction with the traditional measures, because they give complementary information of HR dynamics by uncovering abnormalities or alterations in time series data that are not otherwise apparent (2, 4, 5, 16-19, 31). Recently, dynamical analysis of HR variability, measured by the detrended fluctuation method, has provided more powerful prognostic information than the traditional methods of HR variability (16), and the complexity measure ApEn has been shown to predict the onset of atrial fibrillation (31). A fractal-like process (1/f behavior) is characterized by fluctuations that display scale-invariance (self-similarity) and long-range correlations. Such processes generate irregular and complex fluctuations over multiple time scales with high ApEn values and an alpha 1 value of 1.0. It has been suggested that a high degree of complexity and fractal organization may be a marker of healthy physiological structure and function (3). The breakdown of this scale-invariant fractal organization, as seen in many disease states (4, 5, 17, 18), could lead to either totally uncorrelated randomness or highly predictable (single scale) behavior, both of which may result in a less adaptable system (3).

Interindividual Variation and Determinants of Measures of HR Variability

Although several articles (4, 5, 17, 18) have been published using the fractal (DFA) and complexity (ApEn) methods in various cardiovascular disorders, no large-scale studies addressing the reference values, interindividual variation, or relation to cardiovascular risk factors of these dynamical measures in healthy subjects have been published. Fractal-like correlation properties in R-R interval dynamics and complexity, i.e., values of ~1.0 of alpha 1 and ApEn, were observed here in both women and men. Similar fractal-like behavior in R-R interval dynamics over different time scales has also been described in a previous study (9) on subjects with normal sinus rhythm. The results of this study describe the R-R interval dynamics in standard conditions in a large population sample of middle-aged subjects and show that the fractal and complexity measures of HR variability are within a narrow range in the subjects without the evidence of structural heart disease or systemic hypertension. It should be noted that some of the subjects may have had asymptomatic coronary artery disease, which may influence the HR variability (1, 33). This study was based on a random sample of middle-aged subjects without a history of any cardiovascular disease.

Present observations also confirm the results of previous smaller studies (6, 12, 20, 25) of wide intersubject variation and skewed distribution in the traditional nonspectral and spectral measures of HR variability in healthy subjects. This overall HR variability, analyzed by traditional methods, is partly related to various clinical, lifestyle, and laboratory parameters. Time and frequency domain measures (in absolute units, ms2) had significant relations to age and factors describing abnormalities in metabolic and hemodynamic features, such as blood pressure, glucose metabolism, and lipid values. However, demographic and other factors explained only a small proportion of the interindividual variation of the overall R-R interval variability, approx 10%, suggesting that heritable (29) or other unmeasured factors may determine a substantially large proportion of the R-R interval variation.

The dynamical measures were not related to any demographic, lifestyle, or laboratory values. In previous studies, increasing age has been associated with a reduction in overall HR variability (1, 15, 21), loss of complexity, and altered fractal scaling (11). Previous observations of the age dependence of dynamical measures are based on studies comparing subjects over very wide age ranges. Thus the results of this study including subjects 40-60 yr of age do not allow conclusions that complexity or fractal behavior may not change with aging over decades.

Gender-Related Differences in HR Dynamics

Several previous studies (7, 10, 13, 20) observed gender-related differences in HR variability in healthy subjects. In these studies, men have usually had higher overall HR variability compared with women, particularly in lower frequencies. Only a few studies including small sample sizes have been published on the sex-related differences in R-R interval dynamics assessed with new dynamical measures. Concurrent with these previous preliminary observations (7, 10, 13, 20, 24), we found women to have a lower alpha 1, higher ApEn, and lower LF-to-HF ratio compared with men. Notably, the gender-related differences in some of the HR variability measures, such as ApEn and LF-to-HF ratio, were observed only in the sitting, but not in the supine, position. These observations may be partly due to previously observed differences in baroreflex regulation of HR dynamics. However, the exact mechanisms of the gender-related differences in R-R interval dynamics are not completely known. Possible effects of sex hormones (7, 25) and differences in baseline variables like blood pressure (25) have been speculated. After adjusting for differences in several baseline variables, the observed gender-related differences in HR dynamics remained unchanged here, suggesting that the mechanisms behind gender-related differences are probably more related to hormonal or genetic factors than to differences in lifestyle.

Limitation of the Study

R-R interval dynamics were measured in controlled conditions from short ECG recordings in this study. Most of the studies using new dynamical measures in various cardiovascular diseases have been made from ambulatory 24-h ECG recordings, and the results of this study may not be directly applicable to describe the 24-h R-R interval dynamics of healthy subjects. However, controlled external conditions may provide more reliable data on physiological mechanisms of HR behavior without the problems of unstationarities, noise, and artifacts in the recordings.

In conclusion, the results of this study describe the range and interindividual variation of fractal, complexity, and spectral measures of HR variability in a large middle-aged population without evidence of heart disease. Unlike the traditional time and frequency domain measures, fractal and complexity measures of HR variability were not related to common cardiovascular risk factors or lifestyle. However, there are also gender-related differences in the fractal and complexity measures of HR behavior. These differences are more evident in the sitting than supine position.


    ACKNOWLEDGEMENTS

The excellent work and cooperation of Drs. Asko Rantala, Heikki Kauma, Mauno Lilja, Markku Savolainen, and Antero Kesäniemi is gratefully acknowledged.


    FOOTNOTES

Address for reprint requests and other correspondence: H. V. Huikuri, Dept. of Medicine, Univ. of Oulu, Kajaanintie 50, 90220 Oulu, Finland (E-mail: hhuikuri{at}sun3.oulu.fi).

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.

Received 3 May 1999; accepted in final form 31 October 2000.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION
REFERENCES

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Am J Physiol Heart Circ Physiol 280(3):H1400-H1406
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F. Beckers, B. Verheyden, and A. E. Aubert
Aging and nonlinear heart rate control in a healthy population
Am J Physiol Heart Circ Physiol, June 1, 2006; 290(6): H2560 - H2570.
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 Am. J. Physiol. Heart Circ. Physiol.Home page
A. J. Hautala, T. H. Makikallio, A. Kiviniemi, R. T. Laukkanen, S. Nissila, H. V. Huikuri, and M. P. Tulppo
Cardiovascular autonomic function correlates with the response to aerobic training in healthy sedentary subjects
Am J Physiol Heart Circ Physiol, October 1, 2003; 285(4): H1747 - H1752.
[Abstract] [Full Text] [PDF]

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