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Evaluation of autonomic functions of patients with multiple system atrophy and Parkinson's disease by head-up tilt test.

Chikako WatanoYuri ShiotaKeiichi OnodaAbdullah Md SheikhSeiji MishimaEri NittaShozo YanoShuhei YamaguchiAtsushi Nagai
Published in: Journal of neural transmission (Vienna, Austria : 1996) (2017)
The aim of this study was to evaluate the autonomic neural function in Parkinson's disease (PD) and multiple system atrophy (MSA) with head-up tilt test and spectral analysis of cardiovascular parameters. This study included 15 patients with MSA, 15 patients with PD, and 29 healthy control (HC) subjects. High frequency power of the RR interval (RR-HF), the ratio of low frequency power of RR interval to RR-HF (RR-LF/HF) and LF power of systolic BP were used to evaluate parasympathetic, cardiac sympathetic and vasomotor sympathetic functions, respectively. Both patients with PD and MSA showed orthostatic hypotension and lower parasympathetic function (RR-HF) at tilt position as compared to HC subjects. Cardiac sympathetic function (RR-LF/HF) was significantly high in patients with PD than MSA at supine position. RR-LF/HF tended to increase in MSA and HC, but decreased in PD by tilting. Consequently, the change of the ratio due to tilting (ΔRR-LF/HF) was significantly lower in patients with PD than in HC subjects. Further analysis showed that compared to mild stage of PD, RR-LF/HF at the supine position was significantly higher in advanced stage. By tilting, it was increased in mild stage and decreased in the advanced stage of PD, causing ΔRR-LF/HF to decrease significantly in the advanced stage. Thus, we demonstrated that spectral analysis of cardiovascular parameters is useful to identify sympathetic and parasympathetic disorders in MSA and PD. High cardiac sympathetic function at the supine position, and its reduction by tilting might be a characteristic feature of PD, especially in the advanced stage.
Keyphrases
  • high frequency
  • acute heart failure
  • heart rate variability
  • left ventricular
  • heart failure
  • blood pressure
  • magnetic resonance imaging
  • machine learning
  • heart rate
  • computed tomography
  • deep learning
  • optic nerve