Ju-Yi Chen Yungling Leo Lee Wei-Chuan Tsai Cheng-Han Lee Po-Sheng Chen Yi-Heng Li Liang-Miin Tsai Jyh-Hong Chen Li-Jen Lin

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1 Heart Vessels (2011) 26: DOI /s ORIGINAL ARTICLE Cardiac autonomic functions derived from short-term heart rate variability recordings associated with heart rate recovery after treadmill exercise test in young individuals Ju-Yi Chen Yungling Leo Lee Wei-Chuan Tsai Cheng-Han Lee Po-Sheng Chen Yi-Heng Li Liang-Miin Tsai Jyh-Hong Chen Li-Jen Lin Received: 27 July 2009 / Accepted: 9 April 2010 / Published online: 30 October 2010 Ó Springer 2010 Abstract Analysis of short-term heart rate variability (HRV) may provide useful information about autonomic nervous control of heart rate recovery. We studied 495 individuals (273 men), age range years, submitted to treadmill exercise tests and short-term HRV evaluations over time (standard deviation of the normal-to-normal interval [SDNN], the square root of the mean squared differences of successive normal-to-normal intervals [RMSSD], the number of interval differences of successive normal-to-normal intervals greater than 50 ms [NN50 count], the proportion derived by dividing NN50 count by the total number of normal-to-normal intervals [pnn50]) and frequency (low-frequency power [LF], high-frequency power [HF], total power) domains. Among 495 patients, 106 patients (68 men) were elderly (age C 65 years). Male gender and hypertension were significantly higher in elderly patients. The young patients had higher HRR after exercise. HRR at 4 min (54 ± 13 vs 60 ± 12 beats/min; P = 0.003) was the most significant predictor for positive exercise test result. In the young group, both time domain J.-Y. Chen W.-C. Tsai C.-H. Lee P.-S. Chen Y.-H. Li L.-M. Tsai J.-H. Chen L.-J. Lin (&) Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital, 138 Sheng-Li Road, Tainan 704, Taiwan linl@mail.ncku.edu.tw J.-Y. Chen Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan Y. L. Lee Institute of Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan measures (SDNN: correlation coefficient 0.34, P \ 0.001; RMSSD: correlation coefficient 0.37, P \ 0.001) and frequency domain measures (LF: correlation coefficient 0.21, P \ 0.001; HF: correlation coefficient 0.13, P = 0.01; total power: correlation coefficient 0.22, P \ 0.001) were significantly associated with HRR at 4 min. HRR at 4 min was significantly associated with short-term HRV of time and frequency domains in young individuals, but not elderly ones, receiving treadmill exercise test. Keywords Heart rate recovery Heart rate variability Treadmill exercise test Introduction The heart rate recovery (HRR) after exercise reflects autonomic control of heart rate, and impaired HRR has been associated with less favorable prognosis in patient follow-up [1 8]. The pathophysiological basis of these findings is that autonomic imbalance can increase the risk of experiencing cardiovascular events [9 11]. HRR immediately after exercise is considered as a function of reactivation of the parasympathetic drive, and a decrease in the sympathetic drive [12]. Abnormalities in parasympathetic drive are suggested as a potential pathophysiological link to the association between reduced HRR after treadmill exercise test and increased mortality during the follow-up period [13]. Heart rate variability (HRV) represents one of the most promising quantitative markers of autonomic activity [14]. HRV in time and frequency domains is a noninvasive, convenient tool for evaluation of autonomic nervous physiology [15]. Time domain analysis estimates the variation of differences between successive RR intervals

2 Heart Vessels (2011) 26: through indices developed by statistical methods. Frequency domain analysis estimates respiratory-dependent high-frequency and low-frequency power through spectral analysis. High-frequency power is considered as mediated mainly by vagal activity, while low-frequency power has been suggested as mediated by both sympathetic and parasympathetic modulation [16, 17]. The most commonly used time domains include SDNN, the standard deviation of the normal-to-normal interval; RMSSD, the square root of the mean squared differences of successive normal-to-normal intervals; NN50 count, the number of interval differences of successive normal-to-normal intervals greater than 50 ms; and pnn50, the proportion derived by dividing NN50 count by the total number of normal-to-normal intervals. The most commonly used frequency domains include LF, low-frequency power; HF, high-frequency power; and total power. Short-term (5-min) recording is a reliable tool to obtain these HRV measures [15]. We hypothesized that the different rates of HRR after treadmill exercise test would be associated with different indices of short-term (5-min) heart variability as an expression of the balance between parasympathetic and sympathetic drives. Specifically, we tested the hypothesis that individuals with higher indices of parasympathetic activity would have a higher rate of recovery of heart rate after treadmill exercise test. The aim of this study was to evaluate the association between HRV indices obtained from short-term (5-min) electrocardiographic recordings and HRR after treadmill exercise test in a relatively large sample of subjects. Patients and methods Study population The study cohort consisted of 495 consecutive patients (273 men; mean age 54 ± 12 years) who received a symptom-limited exercise testing combined with a shortterm (5-min) HRV examination for evaluation of suspected coronary artery disease (CAD) between February 2007 and June Exclusion criteria included peripheral vascular occlusion diseases, left bundle-branch block, the pre-excitation syndrome, critical valvular heart diseases, critical pulmonary diseases, implanted pacemaker, frequent premature atrial or ventricular contractions, and atrial fibrillation. We also excluded patients with contraindication to or inability to perform treadmill exercise testing. A review of each patient s chart and a structured interview were conducted to gather data on symptoms, medications, coronary risk factors, and other diagnoses. Traditional risk factors for CAD, including diabetes mellitus, hypertension, hypercholesterolemia, and current smoking, were all carefully evaluated in each patient. Diabetes mellitus was diagnosed if the fasting plasma glucose concentration was [125 mg/dl on two separate occasions or if the subject was treated with insulin or oral hypoglycemic agents. Hypertension was diagnosed if blood pressure was [140/ 90 mmhg on three occasions or if the subject was taking any antihypertensive medication. Hypercholesterolemia was defined as a total serum cholesterol concentration C200 mg/dl or as use of lipid-lowering therapy. Smokers were defined as those who habitually smoked cigarettes (C20 cigarettes/day) at the start of this study. A family history of premature CAD was defined as patients whose parents, siblings, or grandparents had CAD before the age of 55 years in men and 65 years in women. Antihypertension drugs were also carefully reviewed in hypertensive patients. All patients were asked to refrain from taking their medications for 1 day before each test. All patients gave written informed consent for this study, and the study protocol was approved by the Human Research Committee of our hospital. Short-term (5-min) heart rate variability recordings Before any testing, all measurements (including blood pressure and heart rate) were made with the patient supine for 20 min in a quiet, temperature-controlled laboratory at 26 ± 1 C. Version 3.0 of the CheckMyHeart (CMH) HRV analysis software (CheckMyHeart, Taiwan) was used for all transformations and analyses. The total recording time was 5 min. CMH is a single-lead electrocardiography recorder (lead I or lead II is available). Beat-by-beat RR interval values (resolution 4 ms) were obtained from the electrocardiograph (ECG) signals using CMH software. CMH software will reject irregular RR intervals (non-nn interval) automatically. However, CMH software also provides the manual filtering of non-nn intervals. Detrended time series were cubically interpolated and resampled at 1 Hz. After detrending via least-square secondorder polynomial fitting, the power spectral density of RR interval time series was estimated by discrete Fourier transform (DFT). The power in the very-low-frequency (VLF ms 2, Hz), low-frequency (LF ms 2, Hz), high-frequency (HF ms 2, Hz), and total power (TP ms 2, approximately \0.4 Hz) bands were obtained by numerical integration. Spectral analysis was also performed using the autoregressive method (Burg algorithm) with spectral decomposition (Johnsen and Andersen algorithm). Autoregressive model order was set at 16. Spectral powers of the VLF, LF, and HF bands (VLF-AR, LF-AR, and HF-AR) were computed summing the respective spectral components. VLF assessed from 5-min recordings is a dubious measure and should be

3 284 Heart Vessels (2011) 26: avoided when interpreting the power spectral density of short-term ECGs [18]. Components showing \10% of the overall power in the band were ignored as they probably represented pure noise contributions. We also computed time-domain HRV parameters suitable for short-term analysis: SDNN, RMSSD, NN50 count, and pnn50. The RMSSD method is preferred to pnn50 and NN50 count because it has better statistical properties. All four time-domain parameters were also calculated for the detrended R R interval series for all examination periods. Exercise protocol All patients were tested using the standard symptom-limited Bruce s treadmill protocol with a commercially available system (CASE; GE Medical Systems, Milwaukee, WI, USA). All patients were instructed not to eat, drink caffeinated beverages, continue cardioactive medication, or smoke any kind of tobacco for 12 h before testing, and to wear comfortable shoes and loose-fitting clothes for the test. The 12-lead ECG, heart rate response, and blood pressure were recorded at baseline, during each stage of exercise, at peak workload, and at 1-min intervals for 8 min after exercise. The results of the treadmill exercise were reported by three cardiologists blinded to the patients backgrounds. The New York Heart Association (NYHA) functional class was also recorded for each patient according to the achieved metabolic equivalents (METs). One MET is equivalent to 3.5 ml of O 2 kg -1 min -1 of body weight. NYHA functional class I was defined as METs C 7; II as METs was between 5 and 6; and III as METs between 2 and 4. NYHA functional classes II and III were considered impaired exercise capacity. The positive or negative result was defined as the published guideline [19]. For the purpose of interpretation, the PQ junction was chosen as the isoelectric point. The development of 0.10 mv (1 mm) or greater of J-point depression measured from the PQ junction, with a horizontal type or down-sloped type of ST segment, depressed C 0.10 mv for 80 ms after the J point in three consecutive beats with a stable baseline, was considered an abnormal response. When the 80 ms after the J-point measurement was difficult to determine at rapid heart rates, the 60 ms after the J-point measurement was used. Abnormal response in C3 continuous leads was a positive result. After achieving peak workload, the treadmill exercise stopped immediately without a cool-down period. The patients remained in supine position for a period of 8 min, which was considered the recovery period. This period was prolonged in case the symptoms or electrocardiographic changes were persistent. The reduction in heart rate from its value at peak exercise to the rate 1 min later was determined as the HRR 1min (beats min -1 ). The reduction in heart rate from its value at peak exercise to the rate 2 min later was determined as the HRR 2min (beats min -1 ). The reduction in heart rate from its value at peak exercise to the rate 4 min later was determined as the HRR 4min (beats min -1 ). Statistical analysis Continuous variables are presented as mean ± standard deviation (SD). Differences between groups were compared with the use of Student s t-test or Chi-square test, as appropriate. To determine the correlation between the indices of HRV and rates of HRR, we used Pearson s correlation coefficient. After adjustment for diabetes mellitus, hypertension, hyperlipidemia, smoker, b-blocker use, resting heart rate, achieved METs, and development of ischemia on stress testing, we found that rates of HRR were significantly associated with age (data not shown). We therefore divided our population into two groups on the basis of age. The cutoff value of age was 65 years using the definition of elderly by the World Health Organization. Statistical significance was set at P \ 0.05 based on a twosided calculation. All analyses were done using SPSS software, version 11.5 for Windows (SPSS, Chicago, IL, USA). Results In our population, 106 subjects (21%; 68 men) were elderly (age C 65 years) and 389 (79%; 205 men) were young (age \ 65 years). Male gender and history of hypertension were significantly higher in elderly patients. There were no differences in waist circumference, body mass index, other risk factors, or drug history between young and elderly patients (Table 1). The young patients had higher basal heart rate, heart rate at end of exercise, heart rate 1 min after end of exercise, heart rate 2 min after end of exercise, heart rate 4 min after end of exercise, HRR 1min, HRR 2min, HRR 4min, and exercise capacity (Table 2). There was no difference in HRV values, including time domains and frequency domains, except for RMSSD. RMSSD was significantly higher in the elderly group (29 ± 49 vs 22 ± 15 ms; P = 0.04) (Table 2). We further divided the patients as positive or negative treadmill exercise test results (Table 3). Sixty patients were defined with positive results. HRR 4min was found to be the most significant predictor for positive treadmill exercise test result (54 ± 13 vs 60 ± 12 beats min -1 ; P = 0.003).

4 Heart Vessels (2011) 26: Table 1 Baseline characteristics for the study population Factors Young (\65 years) n = 389 Elderly (C65 years) n = 106 P value Results are shown as mean ± SD and number (%) Male 205 (52.7%) 68 (64.2%) 0.04 Waist circumference (cm) 85.0 ± ± Body mass index (kg/m 2 ) 25.3 ± ± Background Diabetes 52 (13.4%) 15 (14.2%) 0.14 Hypertension 148 (38.0%) 59 (55.7%) Hyperlipidemia 147 (37.8%) 40 (37.7%) 0.99 Transient ischemic accident 1 (0.3%) 0 (0.0%) 0.60 Stroke 1 (0.3%) 1 (0.9%) 0.33 Smoker 88 (22.6%) 18 (17.0%) 0.21 Family history of premature coronary artery disease 40 (10.3%) 9 (8.5%) 0.58 Previous myocardial infarction 12 (3.1%) 4 (3.8%) 0.77 b-blocker use 98 (25.2%) 35 (33.0%) 0.11 Angiotensin-converting enzyme inhibitor use 18 (4.6%) 4 (3.8%) 0.70 Nondihydropyridine calcium-channel blocker use 81 (20.8%) 24 (22.6%) 0.66 Statins 62 (15.9%) 18 (17.0%) 0.68 Table 2 Values for variables recorded from treadmill exercise test and short-term (5-min) heart rate variability recordings Values are mean ± SD MET metabolic equivalents, SDNN standard deviation of all NN intervals, RMSSD the square root of the mean of the sum of the squares of differences between adjacent NN intervals, NN50 count number of pairs of adjacent NN intervals differing by more than 50 ms in the entire recording, pnn50 NN50 divided by the total number of all NN intervals, LF power in low-frequency range, HF power in highfrequency range Young (\65 years) Elderly (C65 years) P value Treadmill exercise test Basal heart rate (beats min -1 ) 74 ± ± 11 \0.001 Heart rate at end of exercise (beats min -1 ) 158 ± ± 16 \0.001 Heart rate 1 min after end of exercise (beats min -1 ) 131 ± ± 16 \0.001 Heart rate 2 min after end of exercise (beats min -1 ) 110 ± ± 15 \0.001 Heart rate 4 min after end of exercise (beats min -1 ) 99 ± ± 13 \0.001 HRR 1min (beats min -1 ) 27 ± ± 9 \0.001 HRR 2min (beats min -1 ) 49 ± ± 12 \0.001 HRR 4min (beats min -1 ) 60 ± ± 12 \0.001 Exercise capacity (METs) 10.9 ± ± Heart rate variability Time domains SDNN (ms) 35 ± ± RMSSD (ms) 22 ± ± NN50 count 18 ± ± pnn50 (%) 6 ± 11 7 ± Frequency domains (m s 2 ) LF 203 ± ± HF 157 ± ± Total power 850 ± ± In Table 4, we performed two-variable correlation analyses to assess the association between the HRR 4min and HRV values. In the young group, both time domain measures (SDNN: correlation coefficient 0.34, P \ 0.001; RMSSD: correlation coefficient 0.37, P \ 0.001; NN50 count: correlation coefficient 0.32, P \ 0.001; pnn50: correlation coefficient 0.32, P \ 0.001) and frequency domain measures (LF: correlation coefficient 0.21, P \ 0.001; HF: correlation coefficient 0.13, P = 0.01; total power: correlation coefficient 0.22, P \ 0.001) were significantly

5 286 Heart Vessels (2011) 26: associated with HRR 4min. In the elderly group, only SDNN was found to be associated with HRR 4min (correlation coefficient 0.23, P = 0.02). Discussion In the present study, we showed that cardiac autonomic indices derived from short-term (5-min) HRV recordings were important predictors for HRR after a treadmill exercise test in young individuals. To the best of our knowledge, this is the first study to demonstrate that the components of time and frequency domains are correlated with HRR, which is an important surrogate marker to predict cardiovascular mortality. Also, this correlation disappeared in elderly individuals. The cardiac autonomic function indices cannot serve as a surrogate marker for predicting HRR after exercise. Association between HRR and HRV The prognostic importance of the HRR after exercise has already been well established, in healthy adults [1, 20, 21], in patients with cardiovascular disease [22], in diabetic and nondiabetic populations [23, 24], and in individuals with Table 3 The association between heart rate recovery and treadmill exercise test results Treadmill result Positive Negative P Value Number HRR 1min (beats min -1 ) 24 ± 9 27 ± HRR 2min (beats min -1 ) 46 ± ± HRR 4min (beats min -1 ) 54 ± ± Values are mean ± SD risk factors for cardiovascular disease [25, 26]. HRR 1min [1, 27, 28], HRR 2min [29, 30], and HRR 4min [31] have been well established as clinical predictors for cardiovascular events. Our study also showed that HRR 4min was lower in positive than in negative treadmill exercise test. The intensity of parasympathetic reactivation steadily increased further until 4 min into recovery, after which time parasympathetic effects on heart rate remained relatively constant. One recent study [32] shows that sympathetic withdrawal is not a significant factor in HRR within the first minute. Early HRR after exercise is complex but predominantly due to parasympathetic reactivation, with sympathetic and nonautonomic components probably playing minor roles [33]. The fall in heart rate immediately after exercise is considered to be a function of the reactivation of the parasympathetic nervous system [12]. Whereas there is little contention concerning HF power reflecting primarily parasympathetic influences, LF power has been shown to reflect both sympathetic and parasympathetic influences. Parasympathetic influences are present over the whole range of the HRV spectrum, whereas the sympathetic influence was reflected at about 0.15 Hz [34]. We showed that time domain measures (SDNN, RMSSD, NN50 count, and pnn50) and frequency domain measures (LF and HF) were positively correlated with HRR in the young group. Increased major parasympathetic activity before exercise was associated with higher HRR in the young group, but not in the elderly group, in our study. Whereas the exact mechanism is still open to debate, some reports have shown that increasing age is associated with decreasing vagal tone [35, 36], and others have shown no correlation between increasing age and HF [37 41]. The HF power may form a U-shaped curve with peaks at the extremes and a trough in the center with age [38]. The reliability of HRV in older subjects might need to be reinvestigated [37]. Table 4 Association using Pearson correlation coefficients between the indices of heart rate variability derived from 5-min electrocardiographic recordings and HRR 4min after exercise Total population P value Young (\65 years) P value Elderly (C65 years) P value Time domains SDNN (ms) 0.32 \ \ RMSSD (ms) 0.18 \ \ NN50 count 0.25 \ \ pnn50 (%) 0.24 \ \ Frequency domains (m s 2 ) LF 0.18 \ \ HF Total power 0.17 \ \ SDNN standard deviation of all NN intervals, RMSSD the square root of the mean of the sum of the squares of differences between adjacent NN intervals, NN50 count number of pairs of adjacent NN intervals differing by more than 50 ms in the entire recording, pnn50 NN50 divided by the total number of all NN intervals, LF power in low-frequency range, HF power in high-frequency range

6 Heart Vessels (2011) 26: Study limitations Our study was limited by a cross-sectional rather than a longitudinal design. However, resting short-term HRV measurements still significantly associated with the HRR in patients undergoing the treadmill exercise test. Conclusions Heart rate recovery was significantly associated with the time and frequency domain indices derived from shortterm (5-min) HRV recordings in young individuals, but not elderly ones receiving the treadmill exercise test. References 1. Cole CR, Blackstone EH, Pashkow FJ, Sander CE, Lauer MS (1999) Heart rate recovery immediately after exercise as a predictor of mortality. N Engl J Med 341: Jouvn X, Empana JP, Schwartz PJ, Desnos M, Courbon D, Ducimetière P (2005) Heart rate profile during exercise as a predictor of sudden death. 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