Retrospective case-control study of pre-diagnosis observational and prescription data in Parkinson's disease

There are currently no disease-modifying treatments for Parkinson's disease. Retrospective analysis of routinely collected health data may enable earlier risk detection and identify therapeutic candidates for drug repurposing. We conducted a retrospective case-control study using data from the Clinical Practice Research Datalink. Individuals diagnosed with Parkinson's disease (n = 37,910) were matched by age, sex, and general practice to 113,730 controls. Biometric measures and prescription histories were analysed across two pre-diagnostic windows (5-10 years and 10-20 years before diagnosis). Logistic regression models were used to evaluate associations between biomarkers, medication exposure, and Parkinson's disease incidence. A composite risk score was constructed and assessed using receiver operating characteristic analysis. Individuals who developed Parkinson's disease showed significant deviations in established vascular, metabolic, and inflammatory markers years before diagnosis, including elevated systolic blood pressure, serum urea, and creatinine. The combined risk model demonstrated moderate predictive accuracy (area under the curve = 0.73). Previously reported contrasting associations of salbutamol and propranolol with Parkinson's disease incidence were confirmed. Hormonal therapies and triptan medications were associated with lower disease incidence but were more frequently prescribed to individuals with more favourable biometric risk profiles, suggesting potential confounding. In contrast, glucagon-like peptide-1 receptor agonists were prescribed to individuals with higher underlying risk yet were associated with reduced disease incidence. Integration of biometric and prescription histories improves interpretation of medication-disease associations in population data. These findings support glucagon-like peptide-1 receptor agonists as candidates for further investigation in Parkinson's disease based on observational associations and demonstrate the value of real-world data for early identification of population-level risk patterns and therapeutic discovery in neurodegenerative disease.