Derivation and Validation of a Phenoconversion‐Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder

Abstract

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 [18F]FDG‐PET (18F‐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. [18F]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.

Imaging Biomarkers in Prodromal and Earliest Phases of Parkinson’s Disease

“…In a further step, the Scaled Subprofile Model and Principal Component Analysis (SSM-PCA) was used to predict phenoconversion of iRBD to PD by deriving a conversion pattern to PD. This pattern showed a sensitivity of 87% and a specificity of 72% discriminating converters from nonconverters and this conversion pattern was validated in an independent cohort [60,61]. A multimodal study that combined PD-related pattern expression, DaT imaging, and olfactory tests could show that pattern expression was higher in iRBD with iH and in…”