Purpose To investigate whether the patterns of striatal dopamine depletion could provide prognostic information on the clinical profiles of early-stage Parkinson disease (PD). Methods Approximately 634 patients with drug-naive PD who underwent 18F-FP-CIT PET scans were followed up for at least 2 years. After quantifying dopamine transporter (DAT) availability in each striatal subregion, the patterns of striatal dopamine depletion of each patient were assessed based on (1) the degree of dopamine loss in the other striatal subregions compared to the posterior putamen (inter-subregional ratio [ISR]) and (2) the interhemispheric asymmetry of dopamine loss in the posterior putamen (asymmetry index [AI]). According to their patterns, we assessed the longitudinal changes in l-dopa-equivalent doses and l-dopa-induced dyskinesia (LID)-free times using the linear mixed model and Cox regression model, respectively. Results There was no significant correlation between the ISR and AI values (Pearson correlation coefficient, 0.150). The linear mixed model showed that higher AI values were associated with slower longitudinal increases in l-dopa-equivalent dose across time (P = 0.003), whereas ISR values were not (P = 0.154). The Cox regression model demonstrated that higher ISR values were associated with early development of LID (hazard ratio, 1.693; P = 0.010), whereas AI values were not (P = 0.269). Conclusions The present study demonstrated that the pattern of anterior-to-posterior gradient and right-to-left asymmetry of striatal DAT availability predicted the development of LID and increasing doses of dopaminergic medications.
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Conflicts of interest and sources of funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant no. HI16C1118), and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (grant no. NRF-2018R1D1A1B07048959). None declared to all authors.
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All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging