FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease

Abstract Background The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucosepositron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects. Objectives We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD. Methods Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms. Results Both classifiers discriminated HC from PD (p  2  = 0.24, p  2  = 0.23, p  2  = 0.25, p  2  = 0.16, p  Conclusions Two independent analyses performed in a cohort of mild PD patients replicated key features of the PDRP, confirming that FDG-PET and multivariate classification can provide an objective, sensitive biomarker of disease stage with the potential to detect treatment effects on PD progression.