Investigating the relationship between glymphatic dysfunction and motor symptoms in Parkinson's disease.

Abstract

The glymphatic system is instrumental in cerebral waste drainage, and its impairment is mechanistically linked to the pathogenesis of neurodegenerative diseases like Parkinson's disease (PD). Nevertheless, the relationship between dysfunction in the clearance processes and the intensity of motor symptoms in PD remains inadequately characterized. This study applied the non-invasive Diffusion Tensor Image Analysis along the Perivascular Space technique to quantify the clearance network activity in PD patients compared with healthy controls (HCs), probing its prospects as an imaging biomarker for motor impairment. From January 2024 to June 2025, a total of 43 PD patients, diagnosed according to the clinical diagnostic criteria, along with 31 well-matched HCs, were enrolled. All participants underwent MRI, and the waste-clearance system function was evaluated using the DTI-ALPS index. Motor manifestations in the PD group were systematically rated using the MDS-UPDRS. An independent sample t-test was carried out to compare the disparities in this imaging metric between groups. Correlation and multiple regression analyses were further employed to explore the connections between the imaging metric and parkinsonian motor features. This study demonstrated a significantly lower MRI-derived metric in the PD group (1.19 ± 0.14) relative to HCs (1.27 ± 0.13; p = 0.019). The statistical significance of this difference persisted after covariance adjustment for age, years of education, and drinking history via ANCOVA (B = 0.080, 95% CI [0.015, 0.145], p = 0.016). Pearson correlation analysis revealed significant inverse associations between this radiographic measure and both the total MDS-UPDRS score (r = - 0.418, p = 0.005; Bootstrap 95% BCa CI [- 0.628, - 0.171]) and its motor subsection (Part III) score (r = - 0.424, p = 0.005; Bootstrap 95% BCa CI [- 0.644, - 0.169]). Multiple regression indicated that the imaging metric served as an independent negative predictor of motor symptom severity (standardized β = - 0.420, p = 0.010; B = - 42.45, Bootstrap 95% BCa CI [- 79.17, - 9.91]), accounting independently for 14.6% of the variability in motor severity (ΔR² = 0.146, p = 0.008). These results suggest an association between the imaging metric and the function of the clearance network in PD, as well as a significant correlation with motor disability. These findings warrant further investigation into the metric's potential as a neuroimaging marker for motor impairment.