Movement disorder society criteria for clinically established early Parkinson's disease

Berg, Daniela; Adler, Charles H.; Bloem, Bastiaan R.; Chan, Piu; Gasser, Thomas; Goetz, Christopher G.; Halliday, Glenda M.; Lang, Anthony E.; Lewis, Simon J.G.; Li, Yuan; Liepelt‐Scarfone, Inga; Litvan, Irene; Marek, Kenneth; Maetzler, Corina; Mi, Taomian; Obeso, José A.; Oertel, Wolfgang H.; Olanow, C. Warren; Poewe, Werner; Ríos‐Romenets, Silvia; Schäffer, Eva; Seppi, Klaus; Heim, Beatrice; Slow, Elizabeth; Stern, Matthew B.; Bledsoe, Ian O.; Deuschl, Günther; Postuma, Ronald B.

doi:10.1002/mds.27431

Cited by 125 publications

(81 citation statements)

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“…The data used for this article include the analysis of the baseline dataset for LRRK2 and GBA PD patients enrolled between January 2014 and May 2019 from 33 participating sites worldwide. PD LRRK2 and GBA mutation carriers cohort enrolled male or female participants aged 18 or older with the diagnosis of PD based on established diagnostic criteria, a disease duration less than 7 years at screening, Hoehn and Yahr stage less than 4, and LRRK2 or GBA mutation confirmed by the genetic core. Participants were excluded if they had conditions that precluded safe performance of lumbar puncture.…”

Section: Methodsmentioning

confidence: 99%

Clinical and Dopamine Transporter Imaging Characteristics of Leucine Rich Repeat Kinase 2 (LRRK2) and Glucosylceramidase Beta (GBA) Parkinson's Disease Participants in the Parkinson's Progression Markers Initiative: A Cross‐Sectional Study

et al. 2020

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Background There are limited data on the phenotypic and dopamine transporter (DAT) imaging characterization of the Parkinson's disease (PD) patients with leucine rich kinase 2 (LRRK2) and glucosylceramidase beta (GBA) mutations. Objective The objective of this study was to examine baseline clinical and DAT imaging characteristics in GBA and LRRK2 mutation carriers with early PD compared with sporadic PD. Methods The Parkinson's Progression Markers Initiative is an ongoing observational longitudinal study that enrolled participants with sporadic PD, LRRK2 and GBA PD carriers from 33 sites worldwide. All participants are assessed annually with a battery of motor and nonmotor scales, 123‐I Ioflupane DAT imaging, and biologic variables. Results We assessed 158 LRRK2 (89% G2019S), 80 GBA (89 %N370S), and 361 sporadic PD participants with the mean (standard deviation) disease duration of 2.9 (1.9), 3.1 (2.0), and 2.6 (0.6) years, respectively. When compared with sporadic PD, the GBA PD patients had no difference in any motor, cognitive, or autonomic features. The LRRK2 PD patients had less motor disability and lower rapid eye movement behavior disorder questionnaire scores, but no meaningful difference in cognitive or autonomic features. Both genetic cohorts had a higher score on the impulse control disorders scale when compared with sporadic PD, but no difference in other psychiatric features. Both genetic PD cohorts had less loss of dopamine transporter on DAT imaging when compared with sporadic PD. Conclusions We confirm previous reports of milder phenotype associated with LRRK2‐PD. A previously reported more aggressive phenotype in GBA‐PD is not evident early in the disease in N370s carriers. This observation identifies a window for potential disease‐modifying interventions. Longitudinal data will be essential to define the slope of progression for both genetic cohorts. Trial Registration http://clinicaltrials.gov (NCT01141023). © 2020 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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Section: Methodsmentioning

confidence: 99%

Clinical and Dopamine Transporter Imaging Characteristics of Leucine Rich Repeat Kinase 2 (LRRK2) and Glucosylceramidase Beta (GBA) Parkinson's Disease Participants in the Parkinson's Progression Markers Initiative: A Cross‐Sectional Study

et al. 2020

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“…Parkinson's disease [51] is a common age-related brain disorders defined primarily as a movement disorder accompanied by typical symptoms of resting tremors, rigidity, bradykinesia, and postural instability [52]. For Parkinson's disease, we detected indirect influence have negative effects on results with the same parameter as that for prostate cancer.…”

Section: Ppi Network Of Parkinson's Diseasementioning

confidence: 71%

Disease Associated Protein-Protein Interaction Network Reconstruction Based on Comprehensive Influence Analysis

Zhu¹,

Li²,

Ling³

et al. 2020

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Background: Proteins and their interactions are fundamental to biological systems and they are affecting our body. Functional study of protein networks is becoming increasingly essential to get a deep understanding of proteins and their roles in human life and diseases. Although several methods already exist for protein-protein interaction (PPI) network building, the precise reconstruction of disease associated PPI network remains a challenge. In this paper we introduce a novel concept of comprehensive influence of proteins in network, in which direct and indirect connections are adopted for the calculation of influential effects of a protein with different weights. With the optimized weights, we calculate and select the important proteins and their interactions to reconstruct the PPI network for further validation and confirmation.Results: To evaluate the performance of the method, we compared our model with six existing methods using five standard data sets.Conclusions: The results indicated that our method outperforms the existed ones. We then applied our model to prostate cancer and Parkinson’s disease to predict novel disease associated proteins for the future experimental validation.

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“…Parkinson's disease (Berg, et al, 2018) is a common age-related brain disorders defined primarily as a movement disorder accompanied by typical symptoms of resting tremors, rigidity, bradykinesia, and postural instability (Aarsland, et al, 2017). For Parkinson's disease, we detected indirect influence have negative effects on results with the same parameter as that for prostate cancer.…”

Section: Ppi Network Of Parkinson's Diseasementioning

confidence: 71%

Disease associated protein-protein interaction network reconstruction based on comprehensive influence analysis

Zhu

Ling

et al. 2019

Preprint

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Proteins and their interactions are fundamental to biological system. With the scientific paradigm shifting to systems biology, functional study of proteins from a network viewpoint to get a deep understanding of their roles in human life and diseases being increasingly essential. Although several methods already existed for protein-protein interaction (PPI) network building, the precise reconstruction of disease associated PPI network remains a challenge. In this paper we introduce a novel concept of comprehensive influence of proteins in network, in which direct and indirect connections are adopted for the calculation of influential effects of a protein with different weights. With the optimized weights, we calculate and select the important proteins and their interactions to reconstruct the PPI network for further validation and confirmation. To evaluate the performance of the method, we compared our model with the six existed ones by using five standard data sets. The results indicated that our method outperforms the existed ones. We then applied our model to prostate cancer and Parkinson's disease to predict novel disease associated proteins for the future experimental validation. Author SummaryThe diverse protein-protein interaction networks have dramatic effects on biological system. The disease associated PPI networks are generally reconstructed from experimental data with computational models but with limited accuracy. We developed a novel concept of comprehensive influence of proteins in network for reconstructing the PPI network. Our model outperforms the state-of-the-art ones and we then applied our model to identify novel interactions for further validation. / 21experimental methods and the complexity of biomedical systems, the experimental results often present high false-positive and false-negative rates (Cheng, et al., 2016). Moreover, experimental verification of the PPI network is time consuming and expensive. Therefore, rapid and accurate computational methods represent useful alternative for predicting protein interactions, with such results providing guidance for experiments, including determination of unknown relationships between disease-causing genes and protein interactions (Zhu, et al., 2015). Such knowledge can be useful to the understanding of protein structure and evolution, as well as the overall function of the network and its associated dynamic processes (Cowen, et al., 2017;Hoeng, et al., 2014).The unknown interaction between proteins can be predicted by the theory of complex network. PPI network consists of nodes and edges, with the nodes representing proteins and the edges representing associations or interactions between the two proteins (Gani, et al., 2015). Libennowell et al. considered the influence of common neighbor nodes in the network and introduced similarity indices to include common neighbors (Libennowell and Kleinberg, 2007). Pujari et al. suggested that each attribute of a pair of connected proteins represents different information (Pujari and Kanawati, 2012), and L...

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Movement disorder society criteria for clinically established early Parkinson's disease

Abstract: We describe high-specificity criteria for de novo PD, which are freely available for use in clinical trials. © 2018 International Parkinson and Movement Disorder Society.

Cited by 125 publications

References 10 publications

Clinical and Dopamine Transporter Imaging Characteristics of Leucine Rich Repeat Kinase 2 (LRRK2) and Glucosylceramidase Beta (GBA) Parkinson's Disease Participants in the Parkinson's Progression Markers Initiative: A Cross‐Sectional Study

Clinical and Dopamine Transporter Imaging Characteristics of Leucine Rich Repeat Kinase 2 (LRRK2) and Glucosylceramidase Beta (GBA) Parkinson's Disease Participants in the Parkinson's Progression Markers Initiative: A Cross‐Sectional Study

Disease Associated Protein-Protein Interaction Network Reconstruction Based on Comprehensive Influence Analysis

Disease associated protein-protein interaction network reconstruction based on comprehensive influence analysis

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