Parkinsonism & related disorders · 2025
Sleep phenotypes of α-synucleinopathies and tauopathies with Parkinsonism.
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Briel Nils, Marti Céline, Werth Esther, Poryazova Rositsa, Valko Philipp O, Baumann Christian R, Baumann-Vogel Heide, Schreiner Simon J
Аннотация
INTRODUCTION: In neurodegenerative Parkinsonism, biomarkers of α-synucleinopathy (Syn) or tauopathy (Tau) are an unmet need. Rapid eye movement (REM) sleep behavior disorder (RBD) strongly indicates Syn. However, it remains unknown if sleep features other than RBD could reflect underlying neuropathology. Here we assess sleep phenotypes of Syn or Tau in neurodegenerative Parkinsonism and explore their properties as potential biomarkers.
METHODS: We retrospectively analyzed polysomnography recordings from 198 patients with clinically diagnosed Parkinsonism (20 DLB, 100 PD, 45 MSA, 27 PSP, 6 CBS). We compared sleep features between clinical diagnoses and between Syn (DLB + PD + MSA) and Tau (PSP + CBS) patients. We established linear discriminant analysis-informed parsimonious logistic regression models for differentiating Syn and Tau proteinopathies.
RESULTS: Sleep architecture was more disturbed in Tau compared to Syn patients, with less REM and non-REM stage 2 sleep, lower sleep efficiency, and more wake after sleep onset. Stridor was unique to MSA, with a prevalence of 42 %. Parsimonious modeling identified sleep features sufficient to differentiate Tau from Syn patients; Diagnostic accuracy was robust with RBD (AUC = 0.78) but even higher after adding more polysomnography features (AUC = 0.83) and demographic variables to the model (AUC = 0.9). The best classification model of Syn vs. Tau is available online for exploration and custom data input at SynTauSleepTool.
CONCLUSION: Distinct sleep phenotypes characterize neurodegenerative Parkinsonism with Syn or Tau. Pending pathological confirmation, our data suggests that neurodegeneration could affect sleep-wake regulatory brain systems in a proteinopathy-dependent manner. Sleep phenotypes hold promise as non-invasive biomarkers of Syn or Tau in Parkinsonism.
METHODS: We retrospectively analyzed polysomnography recordings from 198 patients with clinically diagnosed Parkinsonism (20 DLB, 100 PD, 45 MSA, 27 PSP, 6 CBS). We compared sleep features between clinical diagnoses and between Syn (DLB + PD + MSA) and Tau (PSP + CBS) patients. We established linear discriminant analysis-informed parsimonious logistic regression models for differentiating Syn and Tau proteinopathies.
RESULTS: Sleep architecture was more disturbed in Tau compared to Syn patients, with less REM and non-REM stage 2 sleep, lower sleep efficiency, and more wake after sleep onset. Stridor was unique to MSA, with a prevalence of 42 %. Parsimonious modeling identified sleep features sufficient to differentiate Tau from Syn patients; Diagnostic accuracy was robust with RBD (AUC = 0.78) but even higher after adding more polysomnography features (AUC = 0.83) and demographic variables to the model (AUC = 0.9). The best classification model of Syn vs. Tau is available online for exploration and custom data input at SynTauSleepTool.
CONCLUSION: Distinct sleep phenotypes characterize neurodegenerative Parkinsonism with Syn or Tau. Pending pathological confirmation, our data suggests that neurodegeneration could affect sleep-wake regulatory brain systems in a proteinopathy-dependent manner. Sleep phenotypes hold promise as non-invasive biomarkers of Syn or Tau in Parkinsonism.