Potential cerebrospinal fluid metabolomic biomarkers and early prediction model for Parkinson's disease.

OBJECTIVE

To identify key cerebrospinal fluid (CSF) metabolomic biomarkers associated with Parkinson's disease (PD) and prodromal PD, providing insights for intervention strategy development.

METHODS

Six 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.

RESULTS

Sixty-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.

CONCLUSION

The 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.