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Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis

Schalkamp, Ann-Kathrin, Peall, Kathryn J. ORCID:, Harrison, Neil A. ORCID: and Sandor, Cynthia ORCID: 2023. Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis. Nature Medicine 29 , pp. 2048-2056. 10.1038/s41591-023-02440-2

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Parkinson’s disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no 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 Parkinson’s disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson’s disease (n = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson’s disease (n = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population (n = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P = 2.2 × 10−3; lifestyle: AUPRC = 0.03 ± 0.04, P = 2.5 × 10−3; blood biochemistry: AUPRC = 0.01 ± 0.00, P = 4.1 × 10−3; prodromal signs: AUPRC = 0.01 ± 0.00, P = 3.6 × 10−3). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson’s disease and identifying participants for clinical trials of neuroprotective treatments.

Item Type: Article
Date Type: Publication
Status: Published
Schools: MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Neuroscience and Mental Health Research Institute (NMHRI)
Publisher: Nature Research
ISSN: 1078-8956
Date of First Compliant Deposit: 10 July 2023
Date of Acceptance: 5 June 2023
Last Modified: 28 Feb 2024 19:42

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