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Updated: Jul 11, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
Autonomic dysregulation during sleep in Parkinsonian spectrum disorders - A proof of concept
Yeilim Cho1, Daniel J Levendowski2, Christine M Walsh3
1Mental Illness Research Education and Clinical Center, VA Puget Sound Health Care System, Seattle, WA, USA.
Introduction:
Autonomic dysfunction is common in α-synucleinopathies such as Lewy Body dementias (LBD), Parkinson's disease (PD), and isolated REM Sleep Behavior Disorder (iRBD). We analyzed pulse-rate changes during sleep to index autonomic nervous system (ANS) dysfunction in patients with α-synucleinopathies vs. non-synucleinopathy groups expected to have normal ANS function.
Methods:
Patients with LBD (n = 16), PD (PD, n = 14) or iRBD (n = 12) were compared to the non-synucleinopathy groups Alzheimers disease dementia (ADem, n = 26), mild cognitive impairment (MCI, n = 34) or controls (CG, n = 54). Sleep Profiler was used to derive a sleep autonomic activation index (AAI), i.e., ≥6 beat-per-minute increase/decrease, pulse rate coefficient of variation (PR-CV), and automated sleep staging with sleep-spindles and non-REM hypertonia (NRH). Analysis included statistical group comparisons and receiver operating characteristics curves to determine optimal classification of groups.
Results:
AAI and PR-CV were moderately correlated across all recordings (rs = 0.58, P < 0.0001), except in the LBD and PD groups. AAI but not PR-CV differentiated the LBD, PD and iRBD from non-Parkinsonian groups. AAI was decreased in LBD and PD patients compared to the CG (p < 0.003) and MCI (p < 0.03). AAI decreased based on age and its receiver operating characteristic area under the curve ranged from 0.63 to 0.75. AAI had a weak negative correlation to NRH (rs ≤ -0.26) but not sleep-spindles.
Conclusion:
Synucleinopathy-related ANS dysfunction can reasonably discriminate prodromal and manifest PD/LBD diseased groups from non-synucleinopathies. Further studies incorporating AAI into a multivariate classifier of neurodegenerative disorders based on sleep characteristics acquired in the patient's home are planned.
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