EDITORIAL COMMENT
Origin and significance of heart rate variability*
Mikko Tulppo, PhD and
Heikki V. Huikuri, MD, FACC ,*
Merikoski Rehabilitation and Research Center, Oulu, Finland
Division of Cardiology, Department of Medicine, University of Oulu, Oulu, Finland
* Reprint requests and correspondence: Prof. Heikki V. Huikuri, Division of Cardiology, Department of Medicine, University of Oulu, P.O. Box 5000 (Kajaanintie 50), FIN-90014 Oulu, Finland.
heikki.huikuri{at}oulu.fi
Measurement of heart rate variability (HRV) has become an important research tool for both clinical and basic scientists (1,2). The better reproducibility and the possibility of measuring long-range heart rate (HR) fluctuations are advantages of 24-h electrocardiographic (ECG) recordings over short-term recordings in the assessment of HRV (2). Long-range oscillations of HR determine a larger proportion of 24-h HRV than do short-term oscillations. This phenomenon can be demonstrated by plotting the (log) power to (log) frequency of HRV in the region of ultra-low-frequency (ULF) and very-low-frequency (VLF) power spectral bands, where an inverse power-law relationship (1/f dynamics) can be observed between the power and the frequency of R-R interval oscillations (2). Similar characteristics can also be observed at higher frequencies, except that small peaks in the spectrum exist at the frequency of spontaneous blood pressure oscillations and respiratory frequency (2).
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Origin of long-range HRV
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Despite the documented prognostic information obtained by measurements of long-range HRV indexes from 24-h ECG recordings in various patient populations (3–5), the origin of these oscillations and the background of altered long-term HR dynamics are not well established. Lack of this information may be one of the many reasons why the measurement of 24-h HRV has not become a routine tool in clinical practice. From a mathematical viewpoint, long-range HRV, such as standard deviation of the 5-min mean R-R interval (SDANN) and ULF power, can be assumed to be larger when there are large temporal differences in the mean HR between time epochs more than 5 min apart from each other. Another factor that influences the magnitude of these HRV indexes is the average HR itself. The higher the average HR, the lower the HRV, even when the relative fluctuations of HR are similar at high and low HR, respectively. Despite these mathematical assumptions, the reasons for reduced long-range HRV during "free-running" ambulatory conditions have not been systematically studied.
In this issue of the Journal, Roach et al. (6) report their results on the effects of changes in the daily activity levels on long-range HRV indexes such as SDANN and the ULF power spectral component (6). In their study, where activity was strictly controlled, SDANN and ULF power were lower during the "rest day" than the "activity day." Furthermore, the relative contribution of each specific activity to the SDANN value was calculated. On the basis of their observations, the authors conclude that long-range HRV indexes are partly dependent on the range of daily activity (6).
Although changes in activity level have evident influences on various indexes of HRV, some caution should be observed in assuming that long-range HRV indexes could simply be used as "surrogates" of individual daily activity and functional state. Although SDANN and ULF power are influenced by the amount and duration of various daily activities, the type of physical activity may have divergent effects on measures of HRV. This is illustrated in Figure 1, where steady-state exercise (i.e., walking at a constant rate and regular activities in the standing position) resulted in a reduction of SDANN, whereas an incremental exercise with a long-term trend in HR resulted in an increase of SDANN. These examples from a previous study (7) demonstrate that there is complex interaction between the type and duration of physical activity, average HR, and the indexes of long-range HRV.
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Figure 1 Standard deviation of the 5-min mean R-R interval (SDANN) and corresponding heart rate (HR) analyzed from 15 min of supine rest, from 15 min free standing in laboratory, from 15 min steady-state treadmill walking at 4.5 km/h, and finally from incremental treadmill walking (from 4.5 km/h to 6.5 km/h) in four healthy subjects. Data is re-analyzed from the study of Tulppo et al. (7).
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On the basis of the study by Roach et al. (6) and the observations shown in Figure 1, it is evident that range, duration, and type of activity have obvious effects on various measures of HRV. However, these experiments do not give insight into whether the ULF oscillations of heart period are also determined by periodic or quasiperiodic oscillations of HR originating from an intrinsic regulatory system. Further studies will be needed to address this issue. The postulation of an intrinsic physiologic origin is supported by the notion of a dominant ULF power spectral component, even during strictly controlled resting conditions without any physical activity.
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HRV in chronic heart failure (HF)
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Several studies have reported reduced long-range HRV among patients with chronic HF to be associated with increased mortality (5,8–11). However, conclusive understanding of the pathophysiology of the reported alterations in HRV in HF is still lacking. On the basis of the results by Roach et al. (6), it is possible that the reduced long-range HRV in patients with chronic HF is partly due to the reduced range of physical daily activity and the higher HR of the patients without beta-blocking medication. However, it is evident that reduced HRV in HF is also a result of altered cardiac autonomic regulation. This is supported by a large body of evidence documenting autonomic dysfunction consisting of sympathetic activation, parasympathetic withdrawal, and unresponsiveness of peripheral organs in HF (12–15). Chronotropic incompetence has been documented in patients with HF as a specific component of autonomic dysfunction (16,17). This is probably another important determinant of reduced long-range HRV. Thus, the majority of observations suggest that altered autonomic regulation is the main reason for abnormalities in various indexes of HRV in chronic HF. This pathophysiologic link makes it possible to understand why abnormal HR dynamics, reflecting altered cardiac autonomic regulation, are also associated with vulnerability to life-threatening arrhythmias.
Practical usage of Holter recordings in the assessment of HRV is confounded by the large number of measures and indexes that have been introduced to provide prognostic information. In old post-infarction series, long-range HRV indexes seemed to yield the most powerful prognostic information (3,18). Recent studies have shown that a more specific abnormality of HRV is the blunted or absent low-frequency oscillation of HR in HF (19). Methods of analysis based on fractals and non-linear dynamics have shown that abnormal short-term fractal HR dynamics, reflecting partly reduced low-frequency oscillations of HR, provide the most powerful prognostic information among postinfarction patients with left ventricular dysfunction and also among patients with chronic HF (20–22). Finally, altered HR turbulence, also reflecting short-term HR dynamics, seems to have prognostic significance among post-infarction patients (23). Taken together, these recent observations suggest that analysis of short-term HR dynamics may better reflect the abnormalities in intrinsic autonomic regulatory systems and the risk of mortality than do long-range HRV indexes.
It can be concluded that we still have insufficient understanding of the exact underlying mechanisms that induce the observed alterations in HRV in chronic HF. Despite the active research on the various aspects of HRV, new research models and large prospective evaluations will still be needed before widespread application of this technique becomes possible.
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Footnotes
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Supported in part by the Medical Council of the Academy of Finland, Helsinki, Finland.
* Editorials published in the Journal of the American College of Cardiology reflect the views of the authors and do not necessarily represent the views of JACC or the American College of Cardiology. 
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References
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1. Huikuri HV, Mäkikallio TH, Airaksinen KEJ, Mitrani R, Castellanos A, Myerburg RJ. Measurement of heart rate variability: A clinical tool or a research toy? J Am Coll Cardiol. 1999;34:1878–1883[Abstract/Free Full Text]
2. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurements, physiological interpretation, and clinical use. Circulation. 1996;93:1043–1065[Free Full Text]
3. Kleiger RE, Miller JP, Bigger JT Jr., Moss AJMulticenter Post-Infarction Research Group. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol. 1987;89:256–262
4. Tsuji H, Venditti FJ Jr., Manders ES, et al. Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study. Circulation. 1994;90:878–883[Abstract/Free Full Text]
5. Nolan J, Batin PD, Andrews R, et al. Prospective study of heart rate variability and mortality in chronic heart failure: Results of the United Kingdom heart failure evaluation and assessment of risk trial (UK-Heart). Circulation. 1998;98:1510–1516[Abstract/Free Full Text]
6. Roach D, Wilson W, Ritchie D, Sheldon R. Dissection of long-range heart rate variability: controlled induction of prognostic measures by activity in the laboratory. J Am Coll Cardiol 2004;43:2271–7.
7. Tulppo MP, Hautala A, Mäkikallio TH, et al. Effects of aerobic training on heart rate dynamics in sedentary subjects. J Appl Physiol. 2003;95:364–372[Abstract/Free Full Text]
8. Saul JP, Arai Y, Berger RD, Lilly LS, Colucci WS, Cohen RJ. Assessment of autonomic regulation in chronic congestive heart failure by heart rate spectral analysis. Am J Cardiol. 1988;16:1292–1299
9. Stefanelli T, Bergler-Klein J, Globits S, Pacher R, Glogar D. Heart rate behaviour at different stages of congestive heart failure. Eur Heart J. 1992;13:902–907[Abstract/Free Full Text]
10. Casolo GC, Stroder P, Sulla A, Chelucci A, Freni A, Zerauschek M. Heart rate variability and functional severity of congestive heart failure secondary to coronary artery disease. Eur Heart J. 1995;16:360–367[Abstract/Free Full Text]
11. Wijbenga JAM, Baik AHMM, Meij SH, Simoons ML, Malik M. Heart rate variability index in congestive heart failure: Relation to clinical variables and prognosis. Eur Heart J. 1998;19:1719–1724[Abstract/Free Full Text]
12. Guzzetti S, Magatelli R, Borroni E, Mezzetti S. Heart rate variability in chronic heart failure. Auton Neuroscience. 2001;90:102–105
13. Ponikowski P, Anker SD, Chua TP, et al. Oscillatory breathing patterns during wakefulness in patients with chronic heart failure. Clinical implications and role of augmented peripheral chemosensitivity. Circulation. 1999;100:2418–2424[Abstract/Free Full Text]
14. Leimbach WN, Wallin BG, Victor RG, Aylward PE, Sandlof G, Marl AL. Direct evidence from intraneural recordings for increased central sympathetic outflow in patients with heart failure. Circulation. 1986;73:913–919[Abstract/Free Full Text]
15. Malliani A, Pagani M. The role of sympathetic nervous system in congestive heart failure. Eur Heart J Suppl. 1983;4:49–54
16. Fei L, Keeling PJ, Sadoul N, et al. Decreased heart rate variability in patients with congestive heart failure and chronotropic incompetence. Pacing Clin Electrophysiol. 1996;19:477–483[CrossRef][Medline]
17. Roche F, Pichot V, Da Costa A, et al. Chronotropic incompetence response to exercise in congestive heart failure, relationship with the cardiac autonomic status. Clin Physiol. 2001;21(3):335–342[CrossRef][Medline]
18. Bigger JT Jr., Fliess GL, Steinmann RC, Rolnitzky LM, Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation. 1992;85:164–171[Abstract/Free Full Text]
19. van de Bourne P, Montano N, Pagani M, Oren R, Somers VK. Absence of low-frequency variability of sympathetic nerve activity in severe heart failure. Circulation. 1997;95:1449–1454[Abstract/Free Full Text]
20. Huikuri HV, Mäkikallio TH, Peng CK, Goldberger AL, Hintze U, Moller MDiamond Study Group. Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation. 2000;101:47–53[Abstract/Free Full Text]
21. Mäkikallio TH, Huikuri HV, Hintze U, et al. Fractal analysis and time- and frequency-domain measures of heart rate variability as predictors of mortality in patients with heart failure. Am J Cardiol. 2001;87:178–182[CrossRef][Medline]
22. Ho KKL, Moody GB, Peng CK, et al. Predicting survival in heart failure cases and controls using fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation. 1997;96:842–848[Abstract/Free Full Text]
23. Schmidt G, Malik M, Barthel P, et al. Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet. 1999;353:1390–1396[CrossRef][Medline]
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Origin of long-range HRV
HRV in chronic heart...