Effect of heavy exercise on spectral baroreflex sensitivity, heart rate, and blood pressure variability in well-trained humans

International audienceThe aim of the study was to assess the instantaneous spectral components of heart rate variability (HRV) and systolic blood pressure variability (SBPV) and determine the low-frequency (LF) and high-frequency baroreflex sensitivity (HF-BRS) during a graded maximal exercise test. The first hypothesis was that the hyperpnea elicited by heavy exercise could entail a significant increase in HF-SBPV by mechanical effect once the first and second ventilatory thresholds (VTs) were exceeded. It was secondly hypothesized that vagal tone progressively withdrawing with increasing load, HF-BRS could decrease during the exercise test. Fifteen well-trained subjects participated in this study. Electrocardiogram (ECG), blood pressure, and gas exchanges were recorded during a cycloergometer test. Ventilatory equivalents were computed from gas exchange parameters to assess VTs. Spectral analysis was applied on cardiovascular series to compute RR and systolic blood pressure power spectral densities, cross-spectral coherence, gain, and α index of BRS. Three exercise intensity stages were compared below (A1), between (A2), and above (A3) VTs. From A1 to A3, both HF-SBPV (A1 45 ± 6, A2 65 ± 10, and A3 120 ± 23 mm2Hg, P andlt; 0.001) and HF-HRV increased (A1 20 ± 5, A2 23 ± 8, and A340 ± 11 ms2, P andlt; 0.02), maintaining HF-BRS (gain, A1 0.68 ± 0.12, A2 0.63 ± 0.08, and A3 0.57 ± 0.09; α index, A1 0.58 ± 0.08, A2 0.48 ± 0.06, and A3 0.50 ± 0.09 ms/mmHg, not significant). However, LFBRS decreased (gain, A1 0.39 ± 0.06, A2 0.17 ± 0.02, and A3 0.11 ± 0.01, P andlt; 0.001; α index, A1 0.46 ± 0.07, A2 0.20 ± 0.02, and A3 0.14 ± 0.01 ms/mmHg, P andlt; 0.001). As expected, once VTs were exceeded, hyperpnea induced a marked increase in both HF-HRV and HF-SBPV. However, this concomitant increase allowed the maintenance of HF-BRS, presumably by a mechanoelectric feedback mechanism. Copyright © 2008 the American Physiological Society

Breathing cardiovascular variability and baroreflex in mechanically ventilated patients

International audienceHeart rate and blood pressure variations during spontaneous ventilation are related to the negative airway pressure during inspiration. Inspiratory airway pressure is positive during mechanical ventilation, suggesting that reversal of the normal baroreflex-mediated pattern of variability may occur. We investigated heart rate and blood pressure variability and baroreflex sensitivity in 17 mechanically ventilated patients. ECG (RR intervals), invasive systolic blood pressure (SBP), and respiratory flow signals were recorded. High-frequency (HF) amplitude of RR and SBP time series and HF phase differences between RR, SBP, and ventilatory signals were continuously computed by Complex DeModulation (CDM). Cross-spectral analysis was used to assess the coherence and the gain functions between RR and SBP, yielding baroreflex sensitivity indices. The HF phase difference between SBP and ventilatory signals was nearly constant in all patients with inversion of SBP variability during the ventilator cycle compared with cycling with negative inspiratory pressure to replicate spontaneous breathing. In 12 patients (group 1), the phase difference between RR and ventilatory signals changed over time and the HF-RR amplitude varied. In the remaining five patients (group 2), RR-ventilatory signal phase and HF-RR amplitude showed little change; however, only one of these patients exhibited a RR-ventilatory signal phase difference mimicking the normal pattern of respiratory sinus arrhythmia. Spectral coherence between RR and SBP was lower in the group with phase difference changes. Positive pressure ventilation exerts mainly a mechanical effect on SBP, whereas its influence on HR variability seems more complex, suggesting a role for neural influences. Copyright © 2008 the American Physiological Society

Relationship between ventilatory thresholds and systolic blood pressure variability

International audienceDuring exercise, an increase in respiratory rate amplifies the blood pressure oscillations. This phenomenon is usually intensified when exercise rate exceeds the ventilatory thresholds (VTs). The present study examined whether VTs assessment was possible from systolic blood pressure variability (SBPV) analysis to give blood pressure ventilatory thresholds (BPVTs). Blood pressure, ECG, and Ventilatory equivalents (VE/VO2, VE/VCO2) were collected from 15 well-trained subjects during an incremental exhaustive test performed on a cycloergometer. The Short-Time Fourier Transform was applied to SBP series to compute the instantaneous high frequency SBPV power (HF-SBPV). BPVTs were determined in all but 3 subjects. For the 12 remaining subjects, visual examination of ventilatory equivalents and HF-SBPV power revealed 2 thresholds for both methods. There was no difference between the first (VT1 235±60 vs. BPVT1 226±55 W, p=0.063) and second (VT2 293±67 vs. BPVT2 301±66 W, p=0.063) thresholds. However, BPVT1 was slightly underestimated compared to VT1 (9.9±15.4 W) given lower limit of agreement (LOA) at 19.9 W and higher at 40.4 W. BPVT2 was over-estimated compared to VT2 (8.8±11.2 W) given lower LOA at 30.9 W and higher at 13.4 W. Thus, BPVTs determination appears useful in conditioning programs with sedentary or pathological subjects but probably not with trained subjects. © 2010 Georg Thieme Verlag KG Stuttgart · New York

Assessment of ventilatory thresholds from heart rate variability in well-trained subjects during cycling.

The purpose of this study was to implement a new method for assessing the ventilatory thresholds from heart rate variability (HRV) analysis. ECG, VO2, VCO2, and VE were collected from eleven well-trained subjects during an incremental exhaustive test performed on a cycle ergometer. The "Short-Term Fourier Transform" analysis was applied to RR time series to compute the high frequency HRV energy (HF, frequency range: 0.15 - 2 Hz) and HF frequency peak (fHF) vs. power stages. For all subjects, visual examination of ventilatory equivalents, fHF, and instantaneous HF energy multiplied by fHF (HF.fHF) showed two nonlinear increases. The first nonlinear increase corresponded to the first ventilatory threshold (VT1) and was associated with the first HF threshold (T(RSA1) from fHF and HFT1 from HF.fHF detection). The second nonlinear increase represented the second ventilatory threshold (VT2) and was associated with the second HF threshold (T(RSA2) from fHF and HFT2 from HF.fHF detection). HFT1 , T(RSA1), HFT2, and T(RSA2) were, respectively, not significantly different from VT1 (VT1 = 219 +/- 45 vs. HFT1 = 220 +/- 48 W, p = 0.975; VT1 vs. T(RSA1) = 213 +/- 56 W, p = 0.662) and VT2 (VT2 = 293 +/- 45 vs. HFT2 = 294 +/- - 48 W, p = 0.956; vs. T(RSA2) = 300 +/- 58 W, p = 0.445). In addition, when expressed as a function of power, HFT1, T(RSA1), HFT2, and T(RSA2) were respectively correlated with VT1 (with HFT1 r2 = 0.94, p < 0.001; with T(RSA1) r2 = 0.48, p < 0.05) and VT2 (with HFT2 r2 = 0.97, p < 0.001; with T(RSA2 )r2 = 0.79, p < 0.001). This study confirms that ventilatory thresholds can be determined from RR time series using HRV time-frequency analysis in healthy well-trained subjects. In addition it shows that HF.fHF provides a more reliable and accurate index than fHF alone for this assessment

Assessment of ventilatory thresholds from heart rate variability in well-trained subjects during cycling.

The purpose of this study was to implement a new method for assessing the ventilatory thresholds from heart rate variability (HRV) analysis. ECG, VO2, VCO2, and VE were collected from eleven well-trained subjects during an incremental exhaustive test performed on a cycle ergometer. The "Short-Term Fourier Transform" analysis was applied to RR time series to compute the high frequency HRV energy (HF, frequency range: 0.15 - 2 Hz) and HF frequency peak (fHF) vs. power stages. For all subjects, visual examination of ventilatory equivalents, fHF, and instantaneous HF energy multiplied by fHF (HF.fHF) showed two nonlinear increases. The first nonlinear increase corresponded to the first ventilatory threshold (VT1) and was associated with the first HF threshold (T(RSA1) from fHF and HFT1 from HF.fHF detection). The second nonlinear increase represented the second ventilatory threshold (VT2) and was associated with the second HF threshold (T(RSA2) from fHF and HFT2 from HF.fHF detection). HFT1 , T(RSA1), HFT2, and T(RSA2) were, respectively, not significantly different from VT1 (VT1 = 219 +/- 45 vs. HFT1 = 220 +/- 48 W, p = 0.975; VT1 vs. T(RSA1) = 213 +/- 56 W, p = 0.662) and VT2 (VT2 = 293 +/- 45 vs. HFT2 = 294 +/- - 48 W, p = 0.956; vs. T(RSA2) = 300 +/- 58 W, p = 0.445). In addition, when expressed as a function of power, HFT1, T(RSA1), HFT2, and T(RSA2) were respectively correlated with VT1 (with HFT1 r2 = 0.94, p < 0.001; with T(RSA1) r2 = 0.48, p < 0.05) and VT2 (with HFT2 r2 = 0.97, p < 0.001; with T(RSA2 )r2 = 0.79, p < 0.001). This study confirms that ventilatory thresholds can be determined from RR time series using HRV time-frequency analysis in healthy well-trained subjects. In addition it shows that HF.fHF provides a more reliable and accurate index than fHF alone for this assessment