Dataset Information

MiRNA-based signatures in cerebrospinal fluid as potential diagnostic tools for early stage Parkinson's disease.

ABSTRACT: Parkinson's Disease is the second most common neurodegenerative disorder, affecting 1-2% of the elderly population. Its diagnosis is still based on the identification of motor symptoms when a considerable number of dopaminergic neurons are already lost. The development of translatable biomarkers for accurate diagnosis at the earliest stages of PD is of extreme interest. Several microRNAs have been associated with PD pathophysiology. Consequently, microRNAs are emerging as potential biomarkers, especially due to their presence in Cerebrospinal Fluid and peripheral circulation. This study employed small RNA sequencing, protein binding ligand assays and machine learning in a cross-sectional cohort comprising 40 early stage PD patients and 40 well-matched controls. We identified a panel comprising 5 microRNAs (Let-7f-5p, miR-27a-3p, miR-125a-5p, miR-151a-3p and miR-423-5p), with 90% sensitivity, 80% specificity and 82% area under the curve (AUC) for the differentiation of the cohorts. Moreover, we combined miRNA profiles with hallmark-proteins of PD and identified a panel (miR-10b-5p, miR-22-3p, miR-151a-3p and ?-synuclein) reaching 97% sensitivity, 90% specificity and 96% AUC. We performed a gene ontology analysis for the genes targeted by the microRNAs present in each panel and showed the likely association of the models with pathways involved in PD pathogenesis.

SUBMITTER: Dos Santos MCT

PROVIDER: S-EPMC5915128 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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miRNA-based signatures in cerebrospinal fluid as potential diagnostic tools for early stage Parkinson's disease.

Dos Santos Marcia Cristina T MCT Barreto-Sanz Miguel Arturo MA Correia Bruna Renata S BRS Bell Rosie R Widnall Catherine C Perez Luis Tosar LT Berteau Caroline C Schulte Claudia C Scheller Dieter D Berg Daniela D Maetzler Walter W Galante Pedro A F PAF Nogueira da Costa Andre A

Oncotarget 20180403 25

Parkinson's Disease is the second most common neurodegenerative disorder, affecting 1-2% of the elderly population. Its diagnosis is still based on the identification of motor symptoms when a considerable number of dopaminergic neurons are already lost. The development of translatable biomarkers for accurate diagnosis at the earliest stages of PD is of extreme interest. Several microRNAs have been associated with PD pathophysiology. Consequently, microRNAs are emerging as potential biomarkers, e ...[more]

PMID: 29707120

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Project description:Background: The substantial heterogeneity of clinical symptoms and lack of reliable progression markers in Parkinson's disease (PD) present a major challenge in predicting accurate progression and prognoses. Increasing evidence indicates that each component of the neurovascular unit (NVU) and blood-brain barrier (BBB) disruption may take part in many neurodegenerative diseases. Since some portions of CSF are eliminated along the neurovascular unit and across the BBB, disturbing the pathways may result in changes of these substances. Methods: Four hundred seventy-four participants from the Parkinson's Progression Markers Initiative (PPMI) study (NCT01141023) were included in the study. Thirty-six initial features, including general information, brief clinical characteristics and the current year's classical scale scores, were used to build five regression models to predict PD motor progression represented by the coming year's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III score after redundancy removal and recursive feature elimination (RFE)-based feature selection. Then, a threshold range was added to the predicted value for more convenient model application. Finally, we evaluated the CSF and blood biomarkers' influence on the disease progression model. Results: Eight hundred forty-nine cases were included in the study. The adjusted R 2 values of three different categories of regression model, linear, Bayesian and ensemble, all reached 0.75. Models of the same category shared similar feature combinations. The common features selected among the categories were the MDS-UPDRS Part III score, Montreal Cognitive Assessment (MOCA) and Rapid Eye Movement Sleep Behavior Disorder Questionnaire (RBDSQ) score. It can be seen more intuitively that the model can achieve certain prediction effect through threshold range. Biomarkers had no significant impact on the progression model within the data in the study. Conclusions: By using machine learning and routinely gathered assessments from the current year, we developed multiple dynamic models to predict the following year's motor progression in the early stage of PD. These methods will allow clinicians to tailor medical management to the individual and identify at-risk patients for future clinical trials examining disease-modifying therapies.

Project description:ObjectiveWe analyzed the longitudinal profile of Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers in early Parkinson's disease (PD) compared with healthy controls (HCs) and tested baseline CSF biomarkers for prediction of clinical decline in PD.MethodsAmyloid-β 1 to 42 (Aβ42 ), total tau (t-tau) and phosphorylated tau (p-tau) at the threonine 181 position were measured using the high-precision Roche Elecsys electrochemiluminescence immunoassay in all available CSF samples from longitudinally studied patients with PD (n = 416) and HCs (n = 192) followed for up to 3 years in the Parkinson's Progression Markers Initiative (PPMI). Longitudinal CSF and clinical data were analyzed with linear-mixed effects models.ResultsWe found patients with PD had lower CSF t-tau (median = 157.7 pg/mL; range = 80.9-467.0); p-tau (median = 13.4 pg/mL; range = 8.0-40.1), and Aβ42 (median = 846.2 pg/mL; range = 238.8-3,707.0) than HCs at baseline (CSF t-tau median = 173.5 pg/mL; range = 82.0-580.8; p-tau median = 15.4 pg/mL; range = 8.1-73.6; and Aβ42 median = 926.5 pg/mL; range = 239.1-3,297.0; p < 0.05-0.001) and a moderate-to-strong correlation among these biomarkers in both patients with PD and HCs (Rho = 0.50-0.97; p < 0.001). Of the patients with PD, 31.5% had pathologically low levels of CSF Aβ42 at baseline and these patients with PD had lower p-tau levels (median = 10.8 pg/mL; range = 8.0-32.8) compared with 27.7% of HCs with pathologically low CSF Aβ42 (CSF p-tau median = 12.8 pg/mL; range 8.2-73.6; p < 0.03). In longitudinal CSF analysis, we found patients with PD had greater decline in CSF Aβ42 (mean difference = -41.83 pg/mL; p = 0.03) and CSF p-tau (mean difference = -0.38 pg/mL; p = 0.03) at year 3 compared with HCs. Baseline CSF Aβ42 values predicted small but measurable decline on cognitive, autonomic, and motor function in early PD.InterpretationOur data suggest baseline CSF AD biomarkers may have prognostic value in early PD and that the dynamic change of these markers, although modest over a 3-year period, suggest biomarker profiles in PD may deviate from healthy aging. ANN NEUROL 2020;88:574-587.

Dataset Information

MiRNA-based signatures in cerebrospinal fluid as potential diagnostic tools for early stage Parkinson's disease.

Publications

miRNA-based signatures in cerebrospinal fluid as potential diagnostic tools for early stage Parkinson's disease.

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