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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.

INTRODUCTION: Progressive supranuclear palsy (PSP) involves midbrain structures, including the red nucleus (RN), an iron-rich region that appears as a high-contrast area on quantitative susceptibility mapping (QSM). RN may serve as a promising biomarker for differentiating parkinsonism. However, RN deformation in PSP remains elusive. This study aimed to evaluate RN deformation in PSP using coronal QSM images and compare them with those of Parkinson's disease (PD) and healthy controls (HC). METHODS: We evaluated the QSM images of 22 patients with PSP, 37 patients with PD, and 43 HC. We developed a grading system to assess RN deformation on coronal QSM images and classified them into three grades. The midbrain and RN volumes were extracted using distinct approaches, and their relationship with grading was investigated. For validation, coronal QSM images of 16 PSP patients from a different institution were assessed. RESULTS: In PSP, 59 % of the patients displayed a flattened RN of grade 3, which we termed a Rice-Grain Appearance. The volume reductions in midbrain and RN were associated with deformation. Differentiation based on the presence of this appearance yielded a specificity of 1.000 (CI: 1.000-1.000) and sensitivity of 0.591 (0.385-0.796) for distinguishing PSP from others. Secondary dataset also showed that 56 % of patients with PSP were classified as grade 3. CONCLUSION: In coronal QSM images, the flattened RN shape appears to be specific to PSP compared to PD and HC and may serve as a marker to help differentiate PSP in future clinical settings.