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

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.

Quantitative Research of 11C-CFT and 18F-FDG PET in Parkinson’s Disease: A Pilot Study With NeuroQ Software

“…Since the theory of parkinsonism-related patterns was proposed by Eidelberg (Eckert et al, 2005), 18 F-FDG PET has been developed specifically to identify and measure metabolic abnormalities in parkinsonism. Many research showed that FDG PET has a very good ability to differentiate PD, MSA, PSP, and CBD according to the glucose metabolism pattern (Eckert et al, 2005; Huang et al, 2007; Teune et al, 2013; Matthews et al, 2018). The prominent network abnormality detected in PD is characterized by increased pallidothalamic and pontine metabolic activity, associated with reductions in the premotor cortex and parietal associate regions.…”

Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts

“…In collaboration with Eidelberg et al, PDRPs were identified in independent American, Indian, Chinese, and Slovenian populations [11,15,20,21]. Independently from these authors, the PDRP was recently derived in an Israeli population [22]. These PDRPs were highly similar to the PDRP USA , although minor deviations in PDRP regional topography were observed in several of these studies, which may be caused by differences in demographics or clinical characteristics of the cohorts.…”

Reproducible metabolic topographies associated with multiple system atrophy: Network and regional analyses in Chinese and American patient cohorts

“…Despite the clinical heterogeneity of disease manifestations, metabolic activity assessed with 18 F-fluorodeoxyglucose ( 18 F-FDG) PET in conjunction with multivariate computing algorithms has revealed disease-specific brain network patterns in parkinsonism ( Ma et al, 2007 , Eckert et al, 2008 , Teune et al, 2013 , Matthews et al, 2018 ). It can reliably differentiate between various parkinsonian syndromes even during early stages ( Spetsieris et al, 2009 , Tang et al, 2010 , Tripathi et al, 2016 ) by measuring expression scores of multiple disease patterns prospectively in any new subjects.…”