Cognitive impairment in REM-sleep behaviour disorder and individuals at risk of Parkinson's disease.

BACKGROUND: Mild cognitive impairment (MCI) is commonly present at the time of Parkinson's Disease (PD) diagnosis, but its prevalence amongst individuals at increased risk of PD is unclear. METHODS: Cognition was assessed using the Montreal Cognitive Assessment (MoCA) in 208 participants in the PREDICT-PD study, and 25 participants with REM-sleep behaviour disorder (RBD). Prevalence of MCI level I was determined in all participants, and level II MCI in the RBD sub-group. RESULTS: Total MoCA scores were worse in the higher risk than the lower risk group defined as those below the 15th percentile of risk (p = 0.009), and in the RBD group compared to all healthy participants (p < 0.001). The prevalence of MCI level I was 12.8% in the lower-risk, 21.9% in the higher-risk (within the highest 15th percentile) and 64% in RBD participants; 66% of RBD participants had MCI level II with multi-domain MCI, but particularly attention and memory deficits. CONCLUSIONS: Cognitive impairment is increased in different groups at higher risk of PD, particularly in the subgroup formally diagnosed with RBD.

The motor prodromes of parkinson's disease: from bedside observation to large-scale application.

There is sufficient evidence that the pathological process that causes Parkinson's disease begins years before the clinical diagnosis is made. Over the last 15 years, there has been much interest in the existence of a prodrome in some patients, with a particular focus on non-motor symptoms such as reduced sense of smell, REM-sleep disorder, depression, and constipation. Given that the diagnostic criteria for Parkinson's disease depends on the presence of bradykinesia, it is somewhat surprising that there has been much less research into the possibility of subtle motor dysfunction as a pre-diagnostic pointer. This review will focus on early motor features and provide some advice on how to detect and measure them.

Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics.

INTRODUCTION: Neurodegenerative parkinsonian syndromes have significant clinical and pathological overlap, making early diagnosis difficult. Cerebrospinal fluid (CSF) biomarkers may aid the differentiation of these disorders, but other than α-synuclein and neurofilament light chain protein, which have limited diagnostic power, specific protein biomarkers remain elusive. OBJECTIVES: To study disease mechanisms and identify possible CSF diagnostic biomarkers through discovery proteomics, which discriminate parkinsonian syndromes from healthy controls. METHODS: CSF was collected consecutively from 134 participants; Parkinson's disease (n = 26), atypical parkinsonian syndromes (n = 78, including progressive supranuclear palsy (n = 36), multiple system atrophy (n = 28), corticobasal syndrome (n = 14)), and elderly healthy controls (n = 30). Participants were divided into a discovery and a validation set for analysis. The samples were subjected to tryptic digestion, followed by liquid chromatography-mass spectrometry analysis for identification and relative quantification by isobaric labelling. Candidate protein biomarkers were identified based on the relative abundances of the identified tryptic peptides. Their predictive performance was evaluated by analysis of the validation set. RESULTS: 79 tryptic peptides, derived from 26 proteins were found to differ significantly between atypical parkinsonism patients and controls. They included acute phase/inflammatory markers and neuronal/synaptic markers, which were respectively increased or decreased in atypical parkinsonism, while their levels in PD subjects were intermediate between controls and atypical parkinsonism. CONCLUSION: Using an unbiased proteomic approach, proteins were identified that were able to differentiate atypical parkinsonian syndrome patients from healthy controls. Our study indicates that markers that may reflect neuronal function and/or plasticity, such as the amyloid precursor protein, and inflammatory markers may hold future promise as candidate biomarkers in parkinsonism.