TY - JOUR
T1 - Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy
AU - Arnaldi, Dario
AU - Mattioli, Pietro
AU - Raffa, Stefano
AU - Pardini, Matteo
AU - Massa, Federico
AU - Iranzo, Alex
AU - Perissinotti, Andres
AU - Niñerola-Baizán, Aida
AU - Gaig, Carles
AU - Serradell, Monica
AU - Muñoz-Lopetegi, Amaia
AU - Mayà, Gerard
AU - Liguori, Claudio
AU - Fernandes, Mariana
AU - Placidi, Fabio
AU - Chiaravalloti, Agostino
AU - Šonka, Karel
AU - Dušek, Petr
AU - Zogala, David
AU - Trnka, Jiri
AU - Boeve, Bradley F.
AU - Miyagawa, Toji
AU - Lowe, Val J.
AU - Miyamoto, Tomoyuki
AU - Miyamoto, Masayuki
AU - Puligheddu, Monica
AU - Figorilli, Michela
AU - Serra, Alessandra
AU - Hu, Michele T.
AU - Klein, Johannes C.
AU - Bes, Frederik
AU - Kunz, Dieter
AU - Cochen De Cock, Valérie
AU - de Verbizier, Delphine
AU - Plazzi, Giuseppe
AU - Antelmi, Elena
AU - Terzaghi, Michele
AU - Bossert, Irene
AU - Kulcsárová, Kristína
AU - Martino, Alessio
AU - Giuliani, Alessandro
AU - Pagani, Marco
AU - Nobili, Flavio
AU - Morbelli, Silvia
N1 - Publisher Copyright:
© 2024 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.
PY - 2024
Y1 - 2024
N2 - 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. ANN NEUROL 2024.
AB - 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. ANN NEUROL 2024.
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U2 - 10.1002/ana.26902
DO - 10.1002/ana.26902
M3 - Article
C2 - 38466158
AN - SCOPUS:85187487552
SN - 0364-5134
JO - Annals of neurology
JF - Annals of neurology
ER -