島 さゆり

BACKGROUND: Weight loss is a substantial non-motor feature of Parkinson's disease (PD) associated with worse clinical outcomes, but the underlying mechanisms remain poorly understood. Thus, we investigated the mechanisms of PD-related weight loss by examining the correlation between body composition and various plasma metabolites. METHODS: We enrolled 91 patients with PD and 47 healthy controls between July 2021 and October 2023. Body composition was evaluated using bioelectrical impedance analysis. Plasma metabolite profiling was conducted via mass spectrometry, including short-chain and medium-chain fatty acids, Krebs cycle intermediates, ketone bodies and phospholipids. Subsequently, alterations in body composition in PD and their association with plasma metabolites were assessed. RESULTS: Patients with PD had lower body weight (p=0.003), body mass index (BMI; p=0.001) and body fat mass (p<0.001) compared with controls. Metabolomic analyses revealed that, in patients with PD, glycolysis and Krebs cycle markers (lactic acid and succinic acid) were reduced, while ketone bodies (acetoacetic acid and 3-hydroxybutyric acid), amino acid catabolism-related markers (2-hydroxybutyric acid and 2-oxobutyric acid) and acetic acid were elevated. Notably, in patients with PD, acetoacetic acid and 3-hydroxybutyric acid negatively correlated with BMI. Phosphatidylcholine (40:2) was also elevated in PD and showed higher levels in individuals at more advanced Hoehn and Yahr stages. CONCLUSIONS: PD-related fat loss was accompanied by a pattern of lower glycolytic activity and higher levels of lipid and amino acid metabolism-related metabolites, consistent with a potential shift in energy utilisation. These findings highlight metabolic pathways as potential targets for interventions to mitigate weight loss in PD.

BACKGROUND: Lysosomal dysfunction is recognized as a key pathological feature of Parkinson's disease (PD); however, its peripheral signatures remain unclear. METHODS: This study evaluated the peripheral profiles of lysosomal hydrolases and their regulation by transcription factor EB (TFEB), focusing on α-galactosidase A (GLA) and β-mannosidase in the peripheral blood mononuclear cells (PBMCs) of 63 PD patients and 44 healthy controls. Lysosomal enzyme activities in PBMC homogenates and serum were quantified using a fluorometric enzymatic assay with kinetic analysis. Protein concentrations were measured by ELISA, and TFEB activation status was evaluated by its phosphorylation level using western blotting. RESULTS: GLA activity and protein concentrations were higher in the PBMCs of patients, but not for β-mannosidase. TFEB protein concentrations were also elevated and showed positive correlations with lysosomal enzyme protein concentrations. TFEB phosphorylation status showed that the ratio of non-phosphorylated to total TFEB did not differ between PD and controls. However, within the PD group, this ratio negatively correlated with TFEB concentrations, suggesting a potential uncoupling between TFEB expression and its functional activation status. Furthermore, both serum-to-PBMC ratios of GLA activity and protein concentration were lower in PD and were associated with PBMC counts, indicating impaired enzyme release from PBMC. CONCLUSIONS: Elevated TFEB expression in PBMCs may reflect a compensatory response to PD-related cellular stress. However, this response may be functionally insufficient due to limited TFEB activity, potentially leading to reduced lysosomal enzyme release. Thus, peripheral TFEB-related lysosomal abnormalities may serve as indicators of systemic autophagy-lysosome dysregulation in PD.

OBJECTIVE: Cerebrospinal fluid (CSF) cell-free mitochondrial DNA (cf-mtDNA) is a potential biomarker for Parkinson's disease (PD), but its clinical relevance remains unclear. We investigated associations between CSF cf-mtDNA levels, body composition, nutritional status, and metabolic biomarkers in PD. METHODS: CSF cf-mtDNA levels, defined as the copy numbers of two regions of the mtDNA circular molecule (mt64-ND1 and mt96-ND5), were quantified in 44 PD patients and 43 controls using multiplex digital PCR. The mt96-ND5/mt64-ND1 ratio was calculated to estimate mtDNA deletion burden. Associations with clinical features, body composition, serum nutritional markers, and plasma energy metabolism-related organic acids were examined. Generalized linear models (GLMs) were performed to adjust for confounders. RESULTS: CSF mt64-ND1 and mt96-ND5 levels were lower in PD patients than controls (p = 0.002, p = 0.001), while the mt96-ND5/mt64-ND1 ratio showed no group difference. GLM analysis identified body composition indices and serum albumin as key determinants of cf-mtDNA levels. Subgroup analysis showed lower cf-mtDNA levels in PD patients with preserved body composition and nutritional status. The mt96-ND5/mt64-ND1 ratio displayed a biphasic association with body composition and an inverse correlation with plasma 2-ketoglutaric acid, suggesting a link to energy metabolism. INTERPRETATION: CSF cf-mtDNA levels are reduced in PD and influenced by body composition and nutritional status, supporting their role as a metabolic biomarker. While the cf-mtDNA deletion ratio remained unchanged, its association with body composition suggests a complex interplay between mitochondrial integrity and metabolism. These findings highlight the relevance of cf-mtDNA in PD pathophysiology and the need for further study.

Objective The development of non-invasive clinical diagnostics is paramount for the early detection of Alzheimer’s disease (AD). Neurofibrillary tangles in AD originate from the entorhinal cortex, a cortical memory area that mediates navigation via path integration (PI). Here, we studied correlations between PI errors and levels of a range of AD biomarkers using a 3D virtual reality navigation system to explore PI as a non-invasive surrogate marker for early detection. Methods We examined 111 healthy adults for PI using a head-mounted 3D VR system, AD-related plasma biomarkers (GFAP, NfL, Aβ40, Aβ42, and p-tau181), Apolipoprotein E (ApoE) genotype, and demographic and cognitive assessments. Covariance of PI and AD biomarkers was assessed statistically, including tests for multivariate linear regression, logistic regression, and predictor importance ranking using machine learning, to identify predictive relationships for PI errors. Results We found significant positive correlations between PI errors with age and plasma GFAP, p-tau181, and NfL levels. Multivariate analysis identified significant correlations of plasma GFAP (t-value = 2.16, p = 0.0332) and p-tau181 (t-value = 2.53, p = 0.0128) with PI errors. Predictor importance ranking using machine learning and receiver operating characteristic curves identified plasma p-tau181 as the most significant predictor of PI. ApoE genotype and plasma p-tau181 showed positive and negative PI associations (ApoE: coefficient = 0.650, p = 0.037; p-tau181: coefficient = −0.899, p = 0.041). EC thickness exhibited negative correlations with age, mean PI errors, and GFAP, NfL, and p-tau181; however, none of these associations remained significant after adjusting for age in linear regression analyses. Conclusion These findings suggest that PI quantified by 3D VR navigation systems may be useful as a surrogate diagnostic tool for the detection of early AD pathophysiology. The hierarchical application of 3D VR PI and plasma p-tau181, in particular, may be an effective combinatorial biomarker for early AD neurodegeneration. These findings advance the application of non-invasive diagnostic tools for early testing and monitoring of AD, paving the way for timely therapeutic interventions and improved epidemiological patient outcomes.