Work

Value of MRI-visible perivascular spaces in predicting levodopa responsiveness of patients with Parkinson’s disease

Year: 2024
Type: article
Study design: Observational Study
Abstract: PURPOSE: Inter-individual difference in levodopa responsiveness is a challenge for physicians to administer personalized treatment for patients with Parkinson's disease (PD). Previous studies demonstrated that magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) might lead to an incomplete response to levodopa. This study aimed to investigate the association between MRI-visible PVS and levodopa responsiveness in patients with PD. METHODS: This cross-sectional study enrolled a total of 327 patients with PD (median age 64.0[57.0-68.0] years, 180 male) who had undergone high-resolution T2-weighted structural MRI at our hospital between 2019 and 2023. An acute levodopa challenge test was performed to evaluate levodopa responsiveness. The patients were divided into two groups: levodopa responsive (MDS-UPDRS-III reduction ≥ 33 %, n = 274) and irresponsive groups (MDS-UPDRS-III reduction < 33 %, n = 53). We employed quantitative and semi-quantitative methods to evaluate MRI-visible PVS in patients with PD, including PVS number, volume fraction, and visual score. Additionally, the imaging features of the levodopa-responsive and irresponsive groups were compared. RESULTS: There were no significant differences in PVS number, volume fraction, and visual score between the levodopa-responsive and -irresponsive groups. The indicators from quantitative and semi-quantitative analyses of PVS were not found to be independent predictors of levodopa responsiveness. None of the indicators from the quantitative or semi-quantitative analyses of PVS were significantly associated with poor responsiveness to levodopa treatment. CONCLUSIONS: MRI-visible PVS are not independently associated with levodopa responsiveness, and their value in predicting levodopa responsiveness in patients with PD is limited.
Authors: Zekai Chen, Die Cai, Jichun Yuan, Jiakuan Chen, Xi Zhou, Wenjie He, Jun Xia
Language: English
Subfield: Neurology
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $3,590
OpenAlex ID: W4404625832
FWCI: 0.686
Cites: 40
Cited by: 3
Related to: 10