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- W4310368215 abstract "Abstract Parkinson’s disease (PD) is a progressive neurodegenerative movement disorder with a 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 better test performance in distinguishing both clinically diagnosed PD (N = 153) (area under precision recall curve (AUPRC): 0.14+ 0.04) and prodromal PD (N = 113) up to seven years pre-diagnosis (AUPRC: 0.07+ 0.03) from the general population (N = 33009) than all other modalities tested. Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing PD and identifying subjects for clinical trials of neuroprotective treatments." @default.
- W4310368215 created "2022-12-09" @default.
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- W4310368215 date "2022-11-29" @default.
- W4310368215 modified "2023-10-16" @default.
- W4310368215 title "Wearable devices can identify Parkinson's disease up to 7 years before clinical diagnosis" @default.
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- W4310368215 doi "https://doi.org/10.1101/2022.11.28.22282809" @default.
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