Background and purpose: The identification of reliable diagnostic and prognostic biomarkers for Parkinson's disease (PD) is urgently needed. Here, we explored the potential use of a-synuclein (a-syn) in plasma neuronal exosomes as a biomarker for early PD diagnosis and disease progression. Methods: This study included both cross-sectional and longitudinal designs. The subjects included 36 patients with early-stage PD, 17 patients with advanced PD, 20 patients with idiopathic rapid eye movement sleep behavior disorder and 21 healthy controls (HCs). a-syn levels were measured by electrochemiluminescence immunoassay. A subgroup of patients with early-stage PD (n = 18) participated in a follow-up examination with repeated blood collection and clinical assessments after an average of 22 months. Results: The a-syn levels in plasma neuronal exosomes were significantly higher in patients with early-stage PD compared with HCs (P = 0.007). Differences in a-syn levels between patients with idiopathic rapid eye movement sleep behavior disorder and HCs did not reach statistical significance (P = 0.08). In addition, Spearman correlation analysis revealed that neuronal exosomal a-syn concentrations were correlated with Movement Disorders Society Unified Parkinson's Disease Rating Scale III/(I + II + III) scores, Non-Motor Symptom Questionnaire scores and Sniffin' Sticks 16-item test scores of patients with PD (P < 0.05). After a mean follow-up of 22 months in patients with earlystage PD, a Cox regression analysis adjusted for age and gender showed that longitudinally increased a-syn rather than baseline a-syn levels were associated with higher risk for motor symptom progression in PD (P = 0.039). Conclusions: Our results suggested that a-syn in plasma neuronal exosomes may serve as a biomarker to aid early diagnosis of PD and also as a prognostic marker for PD progression.
BackgroundThe mechanisms underlying the pathogenesis and progression of Parkinson’s disease (PD) remain elusive, but recent opinions and perspectives have focused on whether the inflammation process induced by microglia contributes to α-synuclein-mediated toxicity. Migration of microglia to the substantia nigra (SN) could precede neurodegeneration in A53T mice. We hypothesized that CXCL12 could be a mediator in the α-synuclein-induced migration of microglia.MethodsAfter establishing appropriate animal and cell culture models, we explored the relationship between α-synuclein and CXCL12 in A53T mice, primary microglia, and BV-2 cell lines. We also explored the mechanisms of these interactions and the signaling processes involved in neuroinflammation.ResultsWe confirmed the positive correlation between α-synuclein and CXCL12 in the postmortem brain tissue of PD patients and the upregulated CXCR4 expression in SN microglia of A53T mice. In addition, as expected, α-synuclein increased the production of CXCL12 in microglia via TLR4/IκB-α/NF-κB signaling. Importantly, CXCL12/CXCR4/FAK/Src/Rac1 signaling was shown to be involved in α-synuclein-induced microglial accumulation.ConclusionsOur study suggests that CXCL12 could be a novel target for the prevention of α-synuclein-triggered ongoing microglial responses. Blocking CXCL12/CXCR4 may be a potential therapeutic approach for PD progression.
Due to overlapping tremor features, the medical diagnosis of Parkinson’s disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), ridge classification (Ridge), backpropagation neural network (BP), and convolutional neural network (CNN) were evaluated and compared aiming to better differentiate between PD and ET by using accessible demographics and tremor information of the upper limbs. The tremor information including tremor acceleration and surface electromyogram (sEMG) signals were collected from 398 patients (PD = 257, ET = 141) and then were used to train the established models to separate PD and ET. The performance of the models was evaluated by indices of accuracy and area under the curve (AUC), which indicated the ensemble learning models including RF and XGBoost showed the best overall predictive ability with accuracy above 0.84 and AUC above 0.90. Furthermore, the relative importance of sex, age, four postures, and five tremor features was analyzed and ranked showing that the dominant frequency of sEMG of flexors, the average amplitude of sEMG of flexors, resting posture, and winging posture had a greater impact on the diagnosis of PD, whereas sex and age were less important. These results provide a reference for the intelligent diagnosis of PD and show promise for use in wearable tremor suppression devices.
Autonomic dysfunction is a well-recognised, common non-motor feature in Parkinson's disease (PD). Autonomic symptoms such as constipation, bladder dysfunction, neurogenic orthostatic hypotension (nOH) and sexual dysfunction not only can affect the quality of life of patients but also predict prognosis and survival in PD (De Pablo-Fernandez et al., 2017). Although autonomic dysfunction becomes more prevalent as the disease progresses, it may also occur prior to the onset of motor symptoms or in the early stages of PD (Postuma, Gagnon, Pelletier, & Montplaisir, 2013). Consistent with this, several studies have documented autonomic dysfunction in idiopathic rapid eye movement (REM) sleep behaviour disorder (iRBD), which is a potentially injurious parasomnia characterised by dream-enacting behaviour and loss of muscle atonia during REM sleep. Longitudinal studies have shown that most patients with iRBD eventually develop PD, dementia
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