Imagine trying to predict the outcome of a race by analyzing the fitness levels and running techniques of the participants. In a similar fashion, scientists have investigated how plasma neurodegenerative proteins and electroencephalography (EEG) parameters can be used to anticipate the progression of different subtypes of Parkinson’s disease (PD). The study involved patients with tremor-dominant (TD) or postural instability and gait disorder (PIGD) PD, as well as healthy individuals. By measuring plasma levels of specific proteins and analyzing EEG patterns, researchers observed intriguing correlations between these markers and various clinical assessments. For example, higher levels of β-amyloid 42 (Aβ42) were linked to decreased depression and anxiety scores in PIGD patients. Furthermore, specific EEG characteristic parameters were associated with motor and non-motor symptoms in the TD group. These findings suggest that combining plasma and EEG markers could provide a more comprehensive understanding of PD progression, particularly for the tremor-dominant subtype. To delve deeper into this fascinating research and its potential implications for diagnosing and monitoring Parkinson’s disease, follow the link below!

ObjectiveThe aim of this study was to investigate the correlations of plasma neurodegenerative proteins and electroencephalography (EEG) dynamic functional network (DFN) parameters with disease progression in early Parkinson’s disease (PD) with different motor subtypes, including tremor-dominant (TD) and postural instability and gait disorder (PIGD).MethodsIn our study, 33 patients with PD (21 TD and 12 PIGD) and 33 healthy controls (HCs) were enrolled. Plasma neurofilament light chain (NfL), α-synuclein (α-syn), total-tau (t-tau), β-amyloid 42 (Aβ42), and β-amyloid 40 (Aβ40) levels were measured using an ultrasensitive single-molecule array (Simoa) immunoassay. All the patients with PD underwent EEG quantified by DFN analysis. The motor and non-motor performances were evaluated by a series of clinical assessments. Subsequently, a correlation analysis of plasma biomarkers and EEG measures with clinical scales was conducted.ResultsIn the TD group, plasma NfL exhibited a significant association with MDS-UPDRS III and Montreal Cognitive Assessment (MoCA). A higher Aβ42/40 level was significantly related to a decrease in Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA) in the PIGD group. In terms of the correlation between EEG characteristic parameters and clinical outcomes, trapping time (TT) delta was positively correlated with MDS-UPDRS III and MoCA scores in the TD group, especially in the prefrontal and frontal regions. For other non-motor symptoms, there were significant direct associations of kPLI theta with HAMD and HAMA, especially in the prefrontal region, and kPLI gamma was particularly correlated with Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire (RBDSQ) scores in the prefrontal, frontal, and parietal regions in the TD group. Furthermore, there was a significant positive correlation between plasma t-tau and kPLI, and pairwise correlations were found among plasma NfL, theta TT, and MoCA scores in the TD group.ConclusionThese results provide evidence that plasma neurodegenerative proteins and EEG measures have great potential in predicting the disease progression of PD subtypes, especially for the TD subtype. A combination of these two kinds of markers may have a superposition effect on monitoring and estimating the prognosis of PD subtypes and deserves further research in larger, follow-up PD cohorts.

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