Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F24%3A10480012" target="_blank" >RIV/00216208:11110/24:10480012 - isvavai.cz</a>
Alternative codes found
RIV/00064165:_____/24:10480012
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=a4eCZr_lve" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=a4eCZr_lve</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/ana.26902" target="_blank" >10.1002/ana.26902</a>
Alternative languages
Result language
angličtina
Original language name
Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy
Original language description
Objective: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). Methods: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 +- 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 +- 7.1 years, 78.4% males) and 160 controls (68.2 +- 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. Results: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. Interpretation: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Annals of Neurology
ISSN
0364-5134
e-ISSN
1531-8249
Volume of the periodical
95
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
1178-1192
UT code for WoS article
001184868100001
EID of the result in the Scopus database
2-s2.0-85187487552