Abstract
BackgroundThe 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 fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects.ObjectivesWe sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD.MethodsTwo 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.ResultsBoth classifiers discriminated HC from PD (p < 0.01, p < 0.03), and classifier scores for age- and gender- matched HC and PD correlated with Hoehn & Yahr stage (R2 = 0.24, p < 0.015) and UPDRS (R2 = 0.23, p < 0.018). Metabolic patterns were highly similar, with hypometabolism in parieto-occipital and prefrontal regions and hypermetabolism in cerebellum, pons, thalamus, paracentral gyrus, and lentiform nucleus relative to whole brain, consistent with the PDRP. An additional classifier was developed using only PD subjects, resulting in scores that correlated with UPDRS (R2 = 0.25, p < 0.02) and Hoehn & Yahr stage (R2 = 0.16, p < 0.06).ConclusionsTwo 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.
Highlights
The complex effects of Parkinson disease (PD), which include multifaceted motor symptoms, cognitive effects, and non-motor symptoms, pose challenges in measuring treatment effect upon disease progression
Mean scores for each PD H& Y stage group were similar to the cutoff of 26 to discriminate normal cognition from impairment identified in the literature (Nasreddine et al, 2005; Hoops et al, 2009; Dalrymple-Alford et al, 2010; Kandiah et al, 2014) and higher than a recently proposed revised cutoff of 23 (Carson et al, 2018)
The key features observed in the present findings are consistent with published studies including hypometabolism in parieto-occipital and premotor/prefrontal cortices and hypermetabolism in cerebellum, pons, thalamus, lentiform nucleus, and paracentral gyrus, relative to whole brain
Summary
The complex effects of Parkinson disease (PD), which include multifaceted motor symptoms, cognitive effects, and non-motor symptoms, pose challenges in measuring treatment effect upon disease progression. The present study sought to determine whether the Parkinson's Disease Related Pattern (PDRP) (Eidelberg et al, 1994; Ma et al, 2007) observed in previously published FDG-PET studies could be independently replicated in early stage PD subjects. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects. 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
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