scholarly journals Effects of postural change from supine to head-up tilt on the skin sympathetic nerve activity component synchronised with the cardiac cycle in warmed men

2016 ◽  
Vol 595 (4) ◽  
pp. 1185-1200 ◽  
Author(s):  
Yu Ogawa ◽  
Yoshi-ichiro Kamijo ◽  
Shigeki Ikegawa ◽  
Shizue Masuki ◽  
Hiroshi Nose
2013 ◽  
Vol 27 (S1) ◽  
Author(s):  
Yu Ogawa ◽  
Yoshi‐ichiro Kamijo ◽  
Shigeki Ikegawa ◽  
Shizue Masuki ◽  
Atsumi Morita ◽  
...  

2015 ◽  
Vol 309 (7) ◽  
pp. H1218-H1224 ◽  
Author(s):  
Fatima El-Hamad ◽  
Elisabeth Lambert ◽  
Derek Abbott ◽  
Mathias Baumert

Beat-to-beat variability of the QT interval (QTV) is sought to provide an indirect noninvasive measure of sympathetic nerve activity, but a formal quantification of this relationship has not been provided. In this study we used power contribution analysis to study the relationship between QTV and muscle sympathetic nerve activity (MSNA). ECG and MSNA were recorded in 10 healthy subjects in the supine position and after 40° head-up tilt. Power spectrum analysis was performed using a linear autoregressive model with two external inputs: heart period (RR interval) variability (RRV) and MSNA. Total and low-frequency power of QTV was decomposed into contributions by RRV, MSNA, and sources independent of RRV and MSNA. Results show that the percentage of MSNA power contribution to QT is very small and does not change with tilt. RRV power contribution to QT power is notable and decreases with tilt, while the greatest percentage of QTV is independent of RRV and MSNA in the supine position and after 40° head-up tilt. In conclusion, beat-to-beat QTV in normal subjects does not appear to be significantly affected by the rhythmic modulations in MSNA following low to moderate orthostatic stimulation. Therefore, MSNA oscillations may not represent a useful surrogate for cardiac sympathetic nerve activity at moderate levels of activation, or, alternatively, sympathetic influences on QTV are complex and not quantifiable with linear shift-invariant autoregressive models.


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