2023-07-03 カーディフ大学
◆研究者たちは、加速度追跡データを使用して、パーキンソン病やパーキンソン前駆期の患者を一般の人々と区別することができた。この発見は、パーキンソン病の早期診断において重要な進歩であり、アクティビティトラッカーやスマートウォッチなどのデバイスが臨床モニタリングに活用できる可能性があることを示唆している。
<関連情報>
- https://www.cardiff.ac.uk/news/view/2729459-identifying-parkinsons-risk-through-smartwatches
- https://www.nature.com/articles/s41591-023-02440-2
ウェアラブルの運動追跡データから、臨床診断の数年前にパーキンソン病が発見される Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis
Ann-Kathrin Schalkamp,Kathryn J. Peall,Neil A. Harrison & Cynthia Sandor
Nature Medicine Published:03 July 2023
DOI:https://doi.org/10.1038/s41591-023-02440-2
Abstract
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.
