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See detailGBA variants in Parkinson’s disease: clinical, metabolomic and multimodal neuroimaging phenotypes
Greuel, Andrea; Trezzi, Jean-Pierre UL; Glaab, Enrico UL et al

in Movement Disorders (in press)

Background: Alterations in the GBA gene (NM_000157.3) are the most important genetic risk factor for Parkinson’s disease. Biallelic GBA mutations cause the lysosomal storage disorder Gaucher´s disease ... [more ▼]

Background: Alterations in the GBA gene (NM_000157.3) are the most important genetic risk factor for Parkinson’s disease. Biallelic GBA mutations cause the lysosomal storage disorder Gaucher´s disease. The GBA variants p.E365K and p.T408M are associated with Parkinson’s but not with Gaucher´s disease. The pathophysiological role of these variants needs to be further explored. Objective: This study analyzed the clinical, neuropsychological, metabolic and neuroimaging phenotypes of Parkinson’s disease patients carrying the GBA variants p.E365K and p.T408M. Methods: GBA was sequenced in 56 mid-stage Parkinson’s disease patients. Carriers of GBA variants were compared to non-carriers regarding clinical history and symptoms, neuropsychological features, metabolomics and multimodal neuroimaging. Blood plasma gas chromatography coupled to mass spectrometry, [18F]FDopa PET, [18F]FDG PET, and resting-state fMRI were performed. Results: Sequence analysis detected 13 heterozygous GBA variant carriers (seven with p.E365K, six 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 Parkinson’s disease-related alterations in carriers versus non-carriers. [18F]FDopa and [18F]FDG PET showed signs of a more advanced disease; [18F]FDG PET and fMRI showed similarities with Lewy body dementia and Parkinson’s disease dementia in carriers. Conclusions: This is the first study to comprehensively assess (neuro-)biological phenotypes of GBA variants in Parkinson’s disease. 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. [less ▲]

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See detailIntegrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease
Glaab, Enrico UL; Trezzi, Jean-Pierre UL; Greuel, Andrea et al

in Neurobiology of Disease (2019), 124(1), 555-562

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 ... [more ▼]

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 and computing receiver operating characteristic (ROC) curves. Results: In the metabolomics data, the baseline comparison between cases and controls as well as the followup 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. [less ▲]

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See detailCombining PET imaging and blood metabolomics data to improve machine learning models for Parkinson’s disease diagnosis
Glaab, Enrico UL; Trezzi, Jean-Pierre UL; Greuel et al

Poster (2018, October 08)

Objective: To investigate whether the integration of PET imaging and metabolomics data can provide improved machine learning models for PD diagnosis. Background: The reliable diagnosis of PD can remain ... [more ▼]

Objective: To investigate whether the integration of PET imaging and metabolomics data can provide improved machine learning models for PD diagnosis. Background: The reliable diagnosis of PD can remain challenging, even at the motor stage. PET imaging can be used to confirm the clinical diagnosis. However, limitations in the robustness of predictive features extracted from the data and the costs associated with PET imaging restrict its application. Using blood metabolomics data as an additional information source may provide improved combined diagnostic models and/or an initial filter to decide on whether to apply PET imaging. Methods: Metabolomics profiling of blood plasma samples using gas chromatography coupled to mass spectrometry (GC­MS) was conducted in 60 IPD patients and 15 healthy controls. After pre-processing, these data were compared to neuroimaging data for subsets of the same individuals using FDOPA PET (44 patients and 14 controls) and FDG PET (51 patients and 15 controls). Machine learning models using linear support vector machines were trained on 50% of the data and evaluated on a 50% hold­out test set using Receiver Operating Characteristic (ROC) curves. Next, standardized FDOPA and FDG PET intensity measurements were combined with those from the metabolomics data to build and evaluate sample classification models in the same manner as for the individual datasets. Results: Both for the FDOPA and FDG PET data, the predictive performance given by the area under the ROC curve (AUC) was highest when combining imaging features with those from the metabolomics data (AUC for FDOPA + metabolomics: 0.98; AUC for FDG + metabolomics: 0.91). The performance was generally lower when using only the respective PET attributes (FDOPA: 0.94, FDG: 0.8) or only the metabolomics data (AUC: 0.66). [less ▲]

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See detailFDG-PET and metabolomics in PD-associated GBA variants
Greuel, Andrea; Trezzi, Jean-Pierre; Glaab, Enrico UL et al

in Movement Disorders (2018), 33(2), 599

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