Wearable biomarkers could predict disease progression in Friedreich’s ataxia

Researchers have shown that a machine-learning approach based on measurements from motion-capture suits can predict individual disease trajectories over a 9-month period, as well as cross-sectional FXN gene expression levels, in nine people with Friedreich’s ataxia.
This method predicted longitudinal disease progression with 1.7- and 4.0-times greater precision compared with Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA) scores, respectively.
The study authors suggest that application of wearable biomarkers in clinical trials could help to overcome the limitations of currently used scales, which require measurement over 18–24 months to evaluate the benefits of potential therapies.