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Derivation and Validation of a Phenoconversion‐Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder
Author(s) -
Mattioli Pietro,
Orso Beatrice,
Liguori Claudio,
Famà Francesco,
Giorgetti Laura,
Donniaquio Andrea,
Massa Federico,
Giberti Andrea,
Vállez García David,
Meles Sanne K.,
Leenders Klaus L.,
Placidi Fabio,
Spanetta Matteo,
Chiaravalloti Agostino,
Camedda Riccardo,
Schillaci Orazio,
Izzi Francesca,
Mercuri Nicola B.,
Pardini Matteo,
Bauckneht Matteo,
Morbelli Silvia,
Nobili Flavio,
Arnaldi Dario
Publication year - 2023
Publication title -
movement disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.352
H-Index - 198
eISSN - 1531-8257
pISSN - 0885-3185
DOI - 10.1002/mds.29236
Subject(s) - posterior cingulate , precuneus , medicine , voxel , psychology , neuroscience , radiology , cortex (anatomy) , functional magnetic resonance imaging
Background Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α‐synucleinopathies. Reliable biomarkers are needed to predict phenoconversion. Objective The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM‐PCA]). Methods Seventy‐six consecutive iRBD patients (70 ± 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 ± 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow‐up time: 21 ± 14 months; 46 nonconverters, 69 ± 6 years, follow‐up time: 33 ± 19 months). All patients underwent [ 18 F]FDG‐PET ( 18 F‐fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM‐PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power. Results First, we derived and validated two distinct center‐specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM‐PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6–21.4). Conclusions We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [ 18 F]FDG‐PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.