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A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia.
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s41591-022-02159-6.pdf | Published version | 4.78 MB | Adobe PDF | View/Open |
Title: | A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia. |
Authors: | Kadirvelu, B Gavriel, C Nageshwaran, S Chan, JPK Nethisinghe, S Athanasopoulos, S Ricotti, V Voit, T Giunti, P Festenstein, R Faisal, AA |
Item Type: | Journal Article |
Abstract: | Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics. |
Issue Date: | 1-Jan-2023 |
Date of Acceptance: | 29-Nov-2022 |
URI: | http://hdl.handle.net/10044/1/99760 |
DOI: | 10.1038/s41591-022-02159-6 |
ISSN: | 1078-8956 |
Publisher: | Nature Research |
Start Page: | 86 |
End Page: | 94 |
Journal / Book Title: | Nature Medicine |
Volume: | 29 |
Issue: | 1 |
Replaces: | 10044/1/102034 http://hdl.handle.net/10044/1/102034 |
Copyright Statement: | © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Publication Status: | Published |
Conference Place: | United States |
Appears in Collections: | Bioengineering Computing Faculty of Medicine Department of Brain Sciences Faculty of Engineering |
This item is licensed under a Creative Commons License