Dataset Information

The Clinical Non-Motor Connectome in Early Parkinson's Disease.

ABSTRACT: BACKGROUND:Non-motor symptoms (NMS) of various anatomical origins are seen in early stage idiopathic Parkinson's disease (IPD). OBJECTIVE:To analyse when and how NMS are linked together at this stage of the disease. METHODS:Prospective study recruiting 64 IPD patients with ?3 years of disease duration and 71 age-matched healthy controls (HC). NMS were clustered in 7 non-motor domains (NMD): general cognition, executive function, visuospatial function, autonomic function, olfaction, mood, and sleep. Correlation coefficients ?|0.3| were considered as significant. Bootstrapped correlation coefficients between the scores were generated in both groups. Fourteen IPD patients and 19 HC were available for a follow-up study two years later. RESULTS:The mean age of both groups was similar. 58% of IPD patients and 37% of HC were male (p?=?0.01). At baseline IPD patients performed less well than HC on all NMD (p value between 0.0001 and 0.02). Out of 91 possible correlations between NMD, 21 were significant in IPD patients and 14 in HC at the level of ?|0.3|. The mean correlation level was higher in IPD patients than in HC, as evidenced by the higher box plot of correlation coefficients. Visuospatial scores at baseline were predictive of the motor deterioration at the follow-up exam. CONCLUSION:At early IPD stage various NMS are linked together, although not connected by anatomical networks. Such a clinical NMD connectome suggests almost synchronous disease initiation at different sites as also supported by fMRI findings. Alternatively, there may be compensation-driven interconnectivity of NMD.

SUBMITTER: Diederich NJ

PROVIDER: S-EPMC7683075 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Clinical Non-Motor Connectome in Early Parkinson's Disease.

Diederich Nico J NJ Sauvageot Nicolas N Pieri Vannina V Hipp Géraldine G Vaillant Michel M

Journal of Parkinson's disease 20200101 4

<h4>Background</h4>Non-motor symptoms (NMS) of various anatomical origins are seen in early stage idiopathic Parkinson's disease (IPD).<h4>Objective</h4>To analyse when and how NMS are linked together at this stage of the disease.<h4>Methods</h4>Prospective study recruiting 64 IPD patients with ≤3 years of disease duration and 71 age-matched healthy controls (HC). NMS were clustered in 7 non-motor domains (NMD): general cognition, executive function, visuospatial function, autonomic function, ol ...[more]

PMID: 32925095

Similar Datasets

Project description:Dopamine transporter (DAT) imaging is an in vivo tool to assess presynaptic dopaminergic function in the clinical practices of Parkinson's disease (PD). Current clinical practices focused on qualitatively visual interpretation of DAT imaging, whereas quantitative analyses are potentially more helpful when monitoring the progression of PD. Previous cross-sectional studies indicated certain motor and non-motor features were associated with striatal DAT binding, whereas limited data were reported in terms of the longitudinal correlation between clinical features of PD with striatal DAT binding. The purpose of our study is to clarify current and longitudinal correlations between striatal DAT binding and clinical measures. A total of 352 untreated PD individuals and 167 healthy controls with complete baseline clinical measures and neuroimaging data were identified from the Parkinson's Progression and Markers Initiative (PPMI) database. Patients with PD underwent DAT imaging at the screening visit and following months 12, 24, and 48. Multiple linear regression models and linear mixed-effect models were respectively conducted to investigate the cross-sectional and longitudinal correlation between clinical characteristics and DAT binding. Associations between changes in clinical characteristics and changes in DAT binding were further evaluated and the Spearman rank correlation coefficients were reported. In the cross-sectional analysis, baseline striatal DAT binding was significantly associated with the Hoehn and Yahr scale, the Movement Disorder Society-Sponsored Revision of the Unified Parkinson Disease Rating Scale (MDS-UPDRS) scores, the rigidity scores, and the axial scores in PD individuals (false discovery rate [FDR]-adjusted p = 0.0017 for all above). Patients who developed freezing of gait had lower striatal DAT binding (FDR-adjusted p = 0.0161). Healthy controls who had higher tremor scores and suffered more severe olfactory dysfunction had lower striatal DAT binding (FDR-adjusted p = 0.0257 for all above). Longitudinal analysis indicated that baseline severity of rapid-eye-movement sleep behavior disorder was significantly associated with longitudinal striatal DAT binding in patients with PD (FDR-adjusted p = 0.0120). Furthermore, changes in MDS-UPDRS scores and the State-Trait Anxiety Inventory (STAI) scores demonstrated significant correlations with changes in striatal DAT binding over 4 years (p = 0.005 and p = 0.032, respectively). Our findings clarified quantitative associations between certain motor and non-motor features with current and future striatal dopamine binding, suggesting the feasibility of using DAT images as a progression predictive marker for PD. Further studies are needed to investigate correlations between different regional dopamine binding with specific clinical features.

Project description:BackgroundIdentifying individuals with high-risk Parkinson's disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.MethodsWe retrieved the data from the Parkinson's Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis model construction, and cox proportional-hazards models were used to predict non-motor symptom progression. The predictive models were internally validated by correcting measures of predictive performance for "optimism" or overfitting with the bootstrap resampling approach.ResultsFor constructing diagnostic models, the final model reached a high accuracy with an area under the curve (AUC) of 0.93 (95% CI: 0.91-0.96), which included eight variables (age, gender, family history, University of Pennsylvania Smell Inventory Test score, Montreal Cognitive Assessment score, RBD Screening Questionnaire score, levels of cerebrospinal fluid α-synuclein, and SNCA rs356181 polymorphism). For the construction of prognostic models, our results showed that the AUC of the three prognostic models improved slightly with increasing follow-up time. The overall AUCs fluctuated around 0.70. The model validation established good discrimination and calibration for predicting PD onset and progression of non-motor symptoms.ConclusionThe findings of our study facilitate predicting the individual risk at an early stage based on the predictors derived from these models. These predictive models provide relatively reliable information to prevent PD onset and progression. However, future validation analysis is still needed to clarify these findings and provide more insight into the predictive models over more extended periods of disease progression in more diverse samples.

Project description:Changes in the function and microbiome of the upper and lower gastrointestinal tract have been documented in Parkinson's disease (PD), although most studies have examined merely fecal microbiome profiles and patients with advanced disease states. In the present study we sought to identify sensitive and specific biomarkers of changes in the oral microbiome of early stage PD through shotgun metatranscriptomic profiling. We recruited 48 PD subjects and 36 age- and gender-matched healthy controls. Subjects completed detailed assessments of motor, cognitive, balance, autonomic and chemosensory (smell and taste) functions to determine their disease stage. We also obtained a saliva sample for profiling of microbial RNA and host mRNA using next generation sequencing. We found no differences in overall alpha and beta diversity between subject groups. However, changes in specific microbial taxa were observed, including primarily bacteria, but also yeast and phage. Nearly half of our findings were consistent with prior studies in the field obtained through profiling of fecal samples, with others representing highly novel candidates for detection of early stage PD. Testing of the diagnostic utility of the microbiome data revealed potentially robust performance with as few as 11 taxonomic features achieving a cross-validated area under the ROC curve of 0.90 and overall accuracy of 84.5%. Bioinformatic analysis of 167 different metabolic pathways supported shifts in a small set of distinct pathways involved in amino acid and energy metabolism among the organisms comprising the oral microbiome. In parallel with the microbial analysis, we also examined the evidence for changes in human salivary mRNAs in the same subjects. This revealed significant changes in a set of 9 host mRNAs, several of which mapped to various brain functions and showed correlations with some of the significantly changed microbial taxa. Unexpectedly, we also observed robust correlations between many of the microbiota and functional measures, including those reflecting cognition, balance, and disease duration. These results suggest that the oral microbiome may represent a highly-accessible and informative microenvironment that offers new insights in the pathophysiology of early stage PD.

Dataset Information

The Clinical Non-Motor Connectome in Early Parkinson's Disease.

Publications

The Clinical Non-Motor Connectome in Early Parkinson's Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets