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BACKGROUND: Long-term levodopa treatment for Parkinson’s disease (PD) is often complicated by motor fluctuations and dyskinesia. Predictive biomarkers for these debilitating side effects are currently lacking, hindering personalized treatment. OBJECTIVES: This study aimed to characterize the cerebrospinal fluid (CSF) metabolome across the PD continuum, distinguish disease-related from medication-related changes, and identify predictive biomarkers for levodopa-induced motor complications. METHODS: We analyzed targeted CSF metabolomic data from the Parkinson’s Progression Markers Initiative (PPMI) cohort, which included healthy controls, prodromal, and PD participants. Statistical analyses revealed differentially abundant metabolites. The association of the metabolite dopamine 3-O-sulfate (DA3S) with motor complications was assessed using logistic regression and decision tree models. RESULTS: DA3S was the most significantly altered metabolite in PD patients, with its elevation exclusively driven by levodopa treatment. DA3S levels were strongly correlated with levodopa exposure (LEDD) and demonstrated a significant independent association with the development of motor complications. A multivariable model combining DA3S, disease duration, and LEDD was used to predict motor complications, with an AUC of 0.806. A decision tree further confirmed the value of DA3S for risk stratification in specific patient subgroups. CONCLUSIONS: CSF DA3S is a pharmacodynamic marker of central levodopa metabolism and a robust, independent predictor of the onset of motor complications in PD patients. When combined with clinical variables, it facilitates effective risk stratification, providing a novel tool for personalizing therapy to mitigate treatment-related adverse effects.
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