Nuclear Imaging Data-Driven Classification of Parkinson's Disease.

ABSTRACT

BACKGROUND: Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder characterized by motor and nonmotor symptoms. Several features have prognostic importance and have been used as key indicators for identifying clinical subtypes. However, the symptom-based classification approach has limitations with respect to the stability of the obtained subtypes. OBJECTIVES: The purpose of this study was to identify subtypes of PD using nuclear imaging biomarkers targeting the cardiac sympathetic nervous and nigro-striatal systems and to compare patterns of cortical morphological change among obtained subtypes. METHODS: We performed unbiased hierarchical cluster analysis using 123 I-metaiodobenzylguanidine cardiac scintigraphy and 123 I-N-(3-fluoropropyl)-2ß-carbomethoxy-3ß-(4-iodophenyl) nortropane single photon emission computed tomography data for 56 patients with PD. We compared clinical characteristics and the patterns of cortical atrophy in the obtained clusters. RESULTS: Three clusters were identified and showed distinct characteristics in onset ages and dopamine-replacement therapy and deep brain stimulation requirements. According to the characteristics, clusters were classified into two subtypes, namely, "cardio-cortical impairment (CC)" and "dopaminergic-dominant dysfunction (DD)" subtype. The three clusters were named according to subtype and time since onset in which 14 patients were classified as "early DD," 25 as "advanced DD," and 17 as "early CC." Compared with the early DD subtype, the early CC subtype showed parietal-dominant diffuse cortical atrophy and the advanced DD subtype showed left-side predominant mild cortical atrophy. CONCLUSIONS: Nuclear imaging biomarker-based classification can be used to identify clinically and pathologically relevant PD subtypes with distinct disease trajectories. © 2023 International Parkinson and Movement Disorder Society.