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Abstract Continuous, objective monitoring of motor signs and symptoms may help improve tracking of disease progression and treatment response in Parkinson’s disease (PD). This study assessed the analytical and clinical validity of multi-sensor smartwatch measurements in hospitalized and home-based settings (96 patients with PD; mean wear time 19 h/day) using a twice-daily virtual motor examination (VME) at times representing medication OFF/ON states. Digital measurement performance was better during inpatient clinical assessments for composite V-scores than single-sensor–derived features for bradykinesia (Spearman |r|= 0.63, reliability = 0.72), tremor (|r|= 0.41, reliability = 0.65), and overall motor features (|r|= 0.70, reliability = 0.67). Composite levodopa effect sizes during hospitalization were 0.51–1.44 for clinical assessments and 0.56–1.37 for VMEs. Reliability of digital measurements during home-based VMEs was 0.62–0.80 for scores derived from weekly averages and 0.24–0.66 for daily measurements. These results show that unsupervised digital measurements of motor features with wrist-worn sensors are sensitive to medication state and are reliable in naturalistic settings. Trial Registration: Japan Pharmaceutical Information Center Clinical Trials Information (JAPIC-CTI): JapicCTI-194825; Registered June 25, 2019.

Abstract Although various body fluid biomarkers for amyotrophic lateral sclerosis (ALS) have been reported, no biomarkers specifically reflecting abnormalities in axonal excitability indices have currently been established. Capillary electrophoresis time-of-flight mass spectrometry and liquid chromatography time-of-flight mass spectrometry were used to perform a comprehensive metabolome analysis of plasma from seven ALS patients and 20 controls, and correlation analysis with disease phenotypes was then performed in 22 other ALS patients. Additionally, electrophysiological studies of motor nerve axonal excitability were performed in all ALS patients. In the ALS and control groups, levels of various metabolites directly associated with skeletal muscle metabolism, such as those involved in fatty acid β-oxidation and the creatine pathway, were detected. Receiver operating characteristic curve analysis of the top four metabolites (ribose-5-phosphate, N6-acetyllysine, dyphylline, 3-methoxytyrosine) showed high diagnostic accuracy (area under the curve = 0.971) in the ALS group compared with the control group. Furthermore, hierarchical cluster analysis revealed that taurine levels were correlated with the strength-duration time constant, an axonal excitability indicator established to predict survival. No significant effects of diabetes mellitus and treatment (Riluzole and Edaravone) on this relationship were detected in the study. Therefore, plasma taurine is a potential novel axonal excitability-translatable biomarker for ALS.

Abstract Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings and alpha-synuclein deposition), and metabolic changes of sebum. Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n = 15, 50], controls [n = 15, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. This non-invasive gene expression profile of SSL-RNAs may contribute to early PD diagnosis based on the neurodegeneration background.

Abstract Increasing evidence shows that metabolic abnormalities in body fluids are distinguishing features of the pathophysiology of Parkinson’s disease. However, a non-invasive approach has not been established in the earliest or pre-symptomatic phases. Here, we report comprehensive double-cohort analyses of the metabolome using capillary electrophoresis/liquid chromatography mass-spectrometry. The plasma analyses identified 18 Parkinson’s disease-specific metabolites and revealed decreased levels of seven long-chain acylcarnitines in two Parkinson’s disease cohorts (n = 109, 145) compared with controls (n = 32, 45), respectively. Furthermore, statistically significant decreases in five long-chain acylcarnitines were detected in Hoehn and Yahr stage I. Likewise, decreased levels of acylcarnitine(16:0), a decreased ratio of acylcarnitine(16:0) to fatty acid(16:0), and an increased index of carnitine palmitoyltransferase 1 were identified in Hoehn and Yahr stage I of both cohorts, suggesting of initial β-oxidation suppression. Receiver operating characteristic curves produced using 12–14 long-chain acylcarnitines provided a large area of under the curve, high specificity and moderate sensitivity for diagnosing Parkinson’s disease. Our data demonstrate that a primary decrement of mitochondrial β-oxidation and that 12–14 long-chain acylcarnitines decreases would be promising diagnostic biomarkers for Parkinson’s disease.