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.