Closed-Loop Identification of Baroreflex Properties in the Frequency Domain

Influence of angiotensin II and telmisartan on in vivo high-resolution renal arterial impedance in rats

Angiotensin II (ANG II) is known to play an important role in regulating renal hemodynamics. We sought to quantify this effect in an in vivo rat model with high-resolution renal arterial (RA) impedance. This study examines the effects of ANG II and its type 1 receptor blocker telmisartan (TELM) on RA impedance. In baroreflex-deactivated rats, we measured RA pressure (Pr) and blood flow (Fr) during random ventricular pacing to induce pressure fluctuation at three different mean Pr (60, 80, and 100 mmHg). We then estimated RA impedance as the transfer function from Fr to Pr. The RA impedance was found to align with a three-element Windkessel model consisting of proximal (Rp) and distal (Rd) resistance and compliance (C). Our study showed Rd reflected the composite characteristics of afferent and efferent arterioles. Rd increased with increasing Pr under the baseline condition with a slope of 1.03 ± 0.21 (× 10-1) min·mL-1. ANG II significantly increased the slope by 0.72 ± 0.29 (× 10-1) min·mL-1 (P < 0.05) without affecting the intercept. TELM significantly reduced the intercept by 34.49 ± 4.86 (× 10-1) mmHg·min·mL-1 (P < 0.001) from the baseline value of 37.93 ± 13.36 (× 10-1) mmHg·min·mL-1, whereas it did not affect the slope. In contrast, Rp was less sensitive than Rd to ANG II or TELM, suggesting Rp may represent the characteristics of elastic large arteries. Our findings provide valuable insights into the influence of ANG II on the dynamics of the renal vasculature.NEW & NOTEWORTHY This present method of quantifying high-resolution renal arterial impedance could contribute to elucidating the characteristics of renal vasculature influenced by physiological mechanisms, renal diseases, or pharmacological effects. The present findings help construct a lumped-parameter renal hemodynamic model that reflects the influence of angiotensin II.

Short-term dynamic characteristics of diuresis during exogenous pressure perturbations with and without arterial baroreflex control

Although body fluid volume control by the kidneys may be classified as a long-term arterial pressure (AP) control system, it does not necessarily follow that the urine flow (UF) response to changes in AP is slow. We quantified the dynamic characteristics of the UF response to short-term AP changes by changing mean AP between 60 mmHg and 100 mmHg every 10 s according to a binary white noise sequence in anesthetized rats (n = 8 animals). In a baro-on trial (the carotid sinus baroreflex was enabled), the UF response represented the combined synergistic effects of pressure diuresis (PD) and neurally mediated antidiuresis (NMA). In a baro-fix trial (the carotid sinus pressure was fixed at 100 mmHg), the UF response mainly reflected the effect of PD. The UF step response was quantified using the sum of two exponential decay functions. The fast and slow components had time constants of 6.5 ± 3.6 s and 102 ± 85 s (means ± SD), respectively, in the baro-on trial. Although the gain of the fast component did not differ between the two trials (0.49 ± 0.21 vs. 0.66 ± 0.22 µL·min-1·kg-1·mmHg-1), the gain of the slow component was greater in the baro-on than in the baro-fix trial (0.51 ± 0.14 vs. 0.09 ± 0.39 µL·min-1·kg-1·mmHg-1, P = 0.023). The magnitude of NMA relative to PD was calculated to be 32.2 ± 29.8%. In conclusion, NMA contributed to the slow component, and its magnitude was approximately one-third of that of the effect of PD.NEW & NOTEWORTHY We quantified short-term dynamic characteristics of the urine flow (UF) response to arterial pressure (AP) changes using white noise analysis. The UF step response approximated the sum of two exponential decay functions with time constants of ∼6.5 s and 102 s. The neurally mediated antidiuretic (NMA) effect contributed to the slow component of the UF step response, with the magnitude of approximately one-third of that of the pressure diuresis (PD) effect.

Input-size dependence of the baroreflex neural arc transfer characteristics during Gaussian white noise inputs

Although Gaussian white noise (GWN) inputs offer a theoretical framework for identifying higher-order nonlinearity, an actual application to the data of the neural arc of the carotid sinus baroreflex did not succeed in fully predicting the well-known sigmoidal nonlinearity. In the present study, we assumed that the neural arc can be approximated by a cascade of a linear dynamic (LD) component and a nonlinear static (NS) component. We analyzed the data obtained using GWN inputs with a mean of 120 mmHg and standard deviations (SDs) of 10, 20, and 30 mmHg for 15 min each in anesthetized rats (n = 7). We first estimated the linear transfer function from carotid sinus pressure to sympathetic nerve activity (SNA) and then plotted the measured SNA against the linearly predicted SNA. The predicted and measured data pairs exhibited an inverse sigmoidal distribution when grouped into 10 bins based on the size of the linearly predicted SNA. The sigmoidal nonlinearity estimated via the LD-NS model showed a midpoint pressure (104.1 ± 4.4 mmHg for SD of 30 mmHg) lower than that estimated by a conventional stepwise input (135.8 ± 3.9 mmHg, P < 0.001). This suggests that the NS component is more likely to reflect the nonlinearity observed during pulsatile inputs that are physiological to baroreceptors. Furthermore, the LD-NS model yielded higher R2 values compared with the linear model and the previously suggested second-order Uryson model in the testing dataset.NEW & NOTEWORTHY We examined the input-size dependence of the baroreflex neural arc transfer characteristics during Gaussian white noise inputs. A linear dynamic-static nonlinear model yielded higher R2 values compared with a linear model and captured the well-known sigmoidal nonlinearity of the neural arc, indicating that the nonlinear dynamics contributed to determining sympathetic nerve activity. Ignoring such nonlinear dynamics might reduce our ability to explain underlying physiology and significantly limit the interpretation of experimental data.

BaroWavelet: An R-based tool for dynamic baroreflex evaluation through wavelet analysis techniques

BACKGROUND AND OBJECTIVE

Baroreflex sensitivity constitutes an indicator of the function of the baroreceptor control mechanism of blood pressure levels. It can be computed after estimating heart rate and blood pressure variability. We propose a novel tool for the evaluation of baroreflex sensitivity using wavelet analysis methods. This tool, known as BaroWavelet, incorporates an algorithm proposal based on the analysis methodology of the RHRV software package, as well as other conventional techniques. Our objectives are to develop and evaluate the tool, by testing its ability to detect changes in baroreflex sensitivity in humans.

METHODS

The code for this tool was designed in the R programming environment and was organized into two analysis routines and a graphical interface. Simulated recordings of blood pressure and inter-beat intervals were employed for an initial evaluation of the tool in a controlled environment. Finally, similar recordings obtained during supine and orthostatic postural evaluations, from patients that belonged to the open-access EUROBAVAR data set, were analyzed.

RESULTS

BaroWavelet identified the scripted changes of the baroreflex sensitivity in the simulated data. The algorithm proposal was also able to better retain additional information regarding the dynamics of the baroreflex. In the EUROBAVAR subjects, baroreflex sensitivity components were significantly smaller during orthostatism when compared with the supine position.

CONCLUSIONS

BaroWavelet managed to characterize baroreflex dynamics from the recordings, which were consistent with the findings reported in the literature. This demonstrates its effectiveness to perform these analyses. We suggest that this tool may be of use in research and for the evaluation of baroreflex sensitivity with clinical and therapeutic purposes. The new tool is available at the official GitHub repository of the Autonomic Nervous System Unit of the University of Málaga (https://github.com/CIMES-USNA-UMA/BaroWavelet).

Dynamic accentuated antagonism of heart rate control during different levels of vagal nerve stimulation intensity in rats

Accentuated antagonism refers to a phenomenon in which the vagal effect on heart rate (HR) is augmented by background sympathetic tone. The dynamic aspect of accentuated antagonism remains to be elucidated during different levels of vagal nerve stimulation (VNS) intensity. We performed VNS on anesthetized rats (n = 8) according to a binary white noise signal with a switching interval of 500 ms at three different stimulation rates (low-intensity: 0-10 Hz, moderate-intensity: 0-20 Hz, and high-intensity: 0-40 Hz). The transfer function from VNS to HR was estimated with and without concomitant tonic sympathetic nerve stimulation (SNS) at 5 Hz. The asymptotic low-frequency (LF) gain (in beats/min/Hz) of the transfer function increased with SNS regardless of the VNS rate [low-intensity: 3.93 ± 0.70 vs. 5.82 ± 0.65 (P = 0.021), moderate-intensity: 3.87 ± 0.62 vs. 5.36 ± 0.53 (P = 0.018), high-intensity: 4.77 ± 0.85 vs. 7.39 ± 1.36 (P = 0.011)]. Moreover, SNS slightly increased the ratio of high-frequency (HF) gain to the LF gain. These effects of SNS were canceled by the pretreatment of ivabradine, an inhibitor of hyperpolarization-activated cyclic nucleotide-gated channels, in another group of rats (n = 6). Although background sympathetic tone antagonizes the vagal effect on mean HR, it enables finer HR control by increasing the dynamic gain of the vagal HR transfer function regardless of VNS intensity. When interpreting the HF component of HR variability, the augmenting effect from background sympathetic tone needs to be considered.

Advanced Cross-Correlation Function Application to Identify Arterial Baroreflex Sensitivity Variations From Healthy to Diabetes Mellitus

Diabetes mellitus (DM) is a chronic disease characterized by elevated blood glucose levels, which leads over time to serious damage to the heart, blood vessels, eyes, kidneys, and nerves. DM is of two types–types 1 or 2. In type 1, there is a problem with insulin secretion, and in type 2–insulin resistance. About 463 million people worldwide have diabetes, and 80% of the majority live in low- and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. Autonomic neuropathy (AN) is one of the common diabetic complications, leading to failure in blood pressure (BP) control and causing cardiovascular disease. Therefore, early detection of AN becomes crucial to optimize treatment. We propose an advanced cross-correlation function (ACCF) between BP and heart rate with suitable threshold parameters to analyze and detect early changes in baroreflex sensitivity (BRS) in DM with AN (DM+). We studied heart rate (HR) and systolic BP responses during tilt in 16 patients with diabetes mellitus only (DM−), 19 diabetes mellitus with autonomic dysfunction (DM+), and 10 healthy subjects. The ACCF analysis revealed that the healthy and DM groups had different filtered percentages of significant maximum cross-correlation function (CCF) value (p < 0.05), and the maximum CCF value after thresholds was significantly reduced during tilt in the DM+ group (p < 0.05). The maximum CCF index, a parameter for the phase between HR and BP, separated the healthy group from the DM groups (p < 0.05). Due to the maximum CCF index in DM groups being located in the positive range and significantly different from healthy ones, it could be speculated that BRS dysfunction in DM and AN could cause a phase change from lead to lag. ACCF could detect and separate DM+ from DM groups. This fact could represent an advantage of the ACCF algorithm. A common cross-correlation analysis was not easy to distinguish between DM− and DM+. This pilot study demonstrates that ACCF analysis with suitable threshold parameters could explore hidden changes in baroreflex control in DM+ and DM−. Furthermore, the superiority of this ACCF algorithm is useful in distinguishing whether AN is present or not in DM.