Parkinson's disease (PD) is a progressive neurodegenerative movement disorder with a long latent phase and no currently existing disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal PD in the general population and compared this digital biomarker to models based on genetics, lifestyle, blood biochemistry, and prodromal symptoms data. Machine learning models trained using accelerometry data achieved high test performance in distinguishing both clinically diagnosed PD (AUROC: 0.83±0.03, AUPRC: 0.51±0.06) and prodromal PD up to seven years pre-diagnosis (AUROC: 0.81±0.03, AUPRC: 0.37±0.05) from the general population, and outperformed all other modalities tested. Accelerometry is a potentially important, low-cost screening tool for identifying people at risk of developing PD and identifying subjects for clinical trials of neuroprotective treatments.

The authors have declared no competing interest.

The work described here is part-funded by the European Regional Development Fund, administered through the Welsh Government. We are also grateful for the Advanced Research Computing at Cardiff.

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