AUTHOR=Zhang Yifan , Yan Yuexin , Kong Xiangxu , Zhang Haijun , Su Shengyuan 

TITLE=Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson’s disease

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 17 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2025.1582362

DOI=10.3389/fnagi.2025.1582362

ISSN=1663-4365

ABSTRACT=ObjectiveTo identify key cerebrospinal fluid (CSF) metabolomic biomarkers associated with Parkinson’s disease (PD) and prodromal PD, providing insights for intervention strategy development.MethodsSix hundred and thirty-nine participants from the Parkinson’s Progression Markers Initiative (PPMI) cohort were included: 300 PD patients, 112 healthy controls (HC), and 227 prodromal PD patients. Regression analysis and OPLS-DA identified metabolic biomarkers, while pathway analysis examined their relationship to clinical features. An XGBoost-based early prediction model was developed to assess the distinction between PD, prodromal PD, and HC. A two-sample bidirectional Mendelian randomization analysis was performed to examine the association between differential metabolites and Parkinson’s disease.ResultsSixty-four metabolites were significantly altered in PD patients compared to HC, with 58 elevated and 6 reduced. In prodromal PD, 19 metabolites were increased, and 34 were decreased. Key metabolic pathways involved glutathione and amino acid metabolism. Dopamine 3-O-sulfate correlated with PD progression, levodopa-equivalent dose, and non-motor symptoms. The XGBoost model demonstrated high specificity in predicting the onset of PD. The MR analysis results showed a positive correlation between higher genetic predictions of dopamine 3-O-sulfate levels and the risk of Parkinson’s disease. In contrast, the reverse MR analysis found that Parkinson’s disease susceptibility significantly increased dopamine 3-O-sulfate levels.ConclusionThe differential expression of CSF metabolites reveals early cellular metabolic changes, providing insights for early diagnosis and monitoring PD progression. A bidirectional causal relationship exists between genetically determined PD susceptibility and metabolites.