Characterization of the Asymmetry of the Cardiac and Sympathetic Arms of the Baroreflex From Spontaneous Variability During Incremental Head-Up Tilt

Abstract

Hysteresis of the baroreflex (BR) is the result of the different BR sensitivity (BRS) when arterial pressure (AP) rises or falls. This phenomenon has been poorly studied and almost exclusively examined by applying pharmacological challenges and static approaches disregarding causal relations. This study inspects the asymmetry of the cardiac BR (cBR) and vascular sympathetic BR (sBR) in physiological closed loop conditions from spontaneous fluctuations of physiological variables, namely heart period (HP) and systolic AP (SAP) leading to the estimation of cardiac BRS (cBRS) and muscle sympathetic nerve activity (MSNA) and diastolic AP (DAP) leading to the estimation of vascular sympathetic BRS (sBRS). The assessment was carried out in 12 young healthy subjects undergoing incremental head-up tilt with table inclination gradually increased from 0 to 60°. Two analytical methods were exploited and compared, namely the sequence (SEQ) and phase-rectified signal averaging (PRSA) methods. SEQ analysis is based on the detection of joint causal schemes representing the HP and MSNA burst rate delayed responses to spontaneous SAP and DAP modifications, respectively. PRSA analysis averages HP and MSNA burst rate patterns after aligning them according to the direction of SAP and DAP changes, respectively. Since cBRSs were similar when SAP went up or down, hysteresis of cBR was not detected. Conversely, hysteresis of sBR was evident with sBRS more negative when DAP was falling than rising. sBR hysteresis was no longer visible during sympathetic activation induced by the orthostatic challenge. These results were obtained via the SEQ method, while the PRSA technique appeared to be less powerful in describing the BR asymmetry due to the strong association between BRS estimates computed over positive and negative AP variations. This study suggests that cBR and sBR provide different information about the BR control, sBR exhibits more relevant non-linear features that are evident even during physiological changes of AP, and the SEQ method can be fruitfully exploited to characterize the BR hysteresis with promising applications to BR branches different from cBR and sBR.

Keywords: MSNA; autonomic nervous system; baroreflex sequence analysis; cardiovascular control; heart rate variability; hysteresis; muscle sympathetic nerve activity; phase-rectified signal averaging.

FIGURE 1

The simple error bar graphs show cBRS (A–C) and sBRS (D–F) in young healthy subjects undergoing incremental head-up tilt as a function of the sign of, respectively, SAP (A–C) and DAP (D–F) variations. cBRS and sBRS were estimated using three different approaches, namely SEQ (A,D), PRSA (B,E), and nPRSA (C,F) methods. Data were pooled together regardless of the experimental condition (i.e., T0, T20, T30, T40, and T60). The results are presented as mean plus standard deviation. The symbol # indicates p < 0.05 versus positive AP variations.

FIGURE 2

The grouped error bar graphs show cBRS (A–C) and sBRS (D–F) in young healthy subjects undergoing incremental head-up tilt as a function of the experimental condition (i.e., T0, T20, T30, T40, and T60). cBRS and sBRS were estimated using three different approaches, namely SEQ (A,D), PRSA (B,E), and nPRSA (C,F) methods, and reported by separately considering positive (black bars) and negative (white bars) SAP variations in (A–C) and DAP changes in (D–F). The results are presented as mean plus standard deviation. The symbol # indicates a significant change of cBRS (A–C), or sBRS (D–F), versus positive AP variations with p < 0.05 within the same experimental condition (i.e., T0, T20, T30, T40, or T60). The symbol ^∗ indicates a significant change with p < 0.05 versus T0 within the same type of cBRS (A–C), or sBRS (D–F).

FIGURE 3

The scatter plots show the results of the linear correlation analysis between cBRS estimates and the sine of the tilt table angles. Each circle represents the cBRS estimate computed in a subject in the assigned experimental condition. cBRS was estimated via SEQ (A,D), PRSA (B,E), and nPRSA (C,F) methods. The cBRS estimates were obtained by separately considering positive (A–C) and negative (D–F) SAP variations. The linear regression line (solid line) and its 95% confidence interval (dotted lines) are plotted only if the Pearson correlation coefficient is significantly different from 0 with p < 0.05.

FIGURE 4

The scatter plots show the results of the linear correlation analysis between sBRS estimates and the sine of the tilt table angles. Each circle represents the sBRS estimate computed in a subject in the assigned experimental condition. sBRS was estimated via SEQ (A,D), PRSA (B,E), and nPRSA (C,F) methods. The sBRS estimates were obtained by separately considering positive (A–C) and negative (D–F) DAP variations. The linear regression line (solid line) and its 95% confidence interval (dotted lines) are plotted only if the Pearson correlation coefficient is significantly different from 0 with p < 0.05.

FIGURE 5

The scatter plots show the results of linear correlation analysis in the planes (cBRS_SEQ+, cBRS_SEQ-) (A), (cBRS_PRSA+, cBRS_PRSA-) (B) and (cBRS_nPRSA+, cBRS_nPRSA-) (C), (sBRS_SEQ+, sBRS_SEQ-) (D), (sBRS_PRSA+, sBRS_PRSA-) (E), and (sBRS_nPRSA+, sBRS_nPRSA-) (F) in young healthy subjects undergoing incremental head-up tilt. Each circle represents the pair of cBRS (A–C) or sBRS (D–F) estimates computed in a subject in a given experimental condition. Data were pooled together regardless of the experimental condition (i.e., T0, T20, T30, T40, and T60). The linear regression line (solid line) and its 95% confidence interval (dotted lines) are plotted only if the Pearson correlation coefficient is significantly different from 0 with p < 0.05.