GBA Variants in Parkinson's Disease: Clinical, Metabolomic, and Multimodal Neuroimaging Phenotypes.

BACKGROUND: Alterations in the GBA gene (NM_000157.3) are the most important genetic risk factor for Parkinson's disease (PD). Biallelic GBA mutations cause the lysosomal storage disorder Gaucher's disease. The GBA variants p.E365K and p.T408M are associated with PD but not with Gaucher's disease. The pathophysiological role of these variants needs to be further explored. OBJECTIVE: This study analyzed clinical, neuropsychological, metabolic, and neuroimaging phenotypes of patients with PD carrying the GBA variants p.E365K and p.T408M. METHODS: GBA was sequenced in 56 patients with mid-stage PD. Carriers of GBA variants were compared with noncarriers regarding clinical history and symptoms, neuropsychological features, metabolomics, and multimodal neuroimaging. Blood plasma gas chromatography coupled to mass spectrometry, 6-[18 F]fluoro-L-Dopa positron emission tomography (PET), [18 F]fluorodeoxyglucose PET, and resting-state functional magnetic resonance imaging were performed. RESULTS: Sequence analysis detected 13 heterozygous GBA variant carriers (7 with p.E365K, 6 with p.T408M). One patient carried a GBA mutation (p.N409S) and was excluded. Clinical history and symptoms were not significantly different between groups. Global cognitive performance was lower in variant carriers. Metabolomic group differences were suggestive of more severe PD-related alterations in carriers versus noncarriers. Both PET scans showed signs of a more advanced disease; [18 F]fluorodeoxyglucose PET and functional magnetic resonance imaging showed similarities with Lewy body dementia and PD dementia in carriers. CONCLUSIONS: This is the first study to comprehensively assess (neuro-)biological phenotypes of GBA variants in PD. Metabolomics and neuroimaging detected more significant group differences than clinical and behavioral evaluation. These alterations could be promising to monitor effects of disease-modifying treatments targeting glucocerebrosidase metabolism. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease.

BACKGROUND: The diagnosis of Parkinson's disease (PD) often remains a clinical challenge. Molecular neuroimaging can facilitate the diagnostic process. The diagnostic potential of metabolomic signatures has recently been recognized. METHODS: We investigated whether the joint data analysis of blood metabolomics and PET imaging by machine learning provides enhanced diagnostic discrimination and gives further pathophysiological insights. Blood plasma samples were collected from 60 PD patients and 15 age- and gender-matched healthy controls. We determined metabolomic profiles by gas chromatography coupled to mass spectrometry (GC-MS). In the same cohort and at the same time we performed FDOPA PET in 44 patients and 14 controls and FDG PET in 51 patients and 16 controls. 18 PD patients were available for a follow-up exam after one year. Both data sets were analysed by two machine learning approaches, applying either linear support vector machines or random forests within a leave-one-out cross-validation scheme and computing receiver operating characteristic (ROC) curves. RESULTS: In the metabolomics data, the baseline comparison between cases and controls as well as the follow-up assessment of patients pointed to metabolite changes associated with oxidative stress and inflammation. For the FDOPA and FDG PET data, the diagnostic predictive performance (DPP) in the ROC analyses was highest when combining imaging features with metabolomics data (ROC AUC for best FDOPA + metabolomics model: 0.98; AUC for best FDG + metabolomics model: 0.91). DPP was lower when using only PET attributes or only metabolomics signatures. CONCLUSION: Integrating blood metabolomics data combined with PET data considerably enhances the diagnostic discrimination power. Metabolomic signatures also indicate interesting disease-inherent changes in cellular processes, including oxidative stress response and inflammation.