Data-derived wearable digital biomarkers predict Frataxin gene expression levels and longitudinal disease progression in Friedreich’s Ataxia

Abstract Friedreich’s ataxia (FA) is a neurodegenerative disease caused by the epigenetic repression of the Frataxin gene modulating mitochondrial activity in the brain, which has a diffuse phenotypic impact on patients’ motor behavior. Therefore, with current gold-standard clinical scales, it requires 18–24 month-long clinical trials to determine if disease-modifying therapies are at all beneficial. Our high-performance monitoring approach captures the full-movement kinematics from human subjects using wearable body sensor networks from a cohort of FA patients during their regular clinical visits. We then use artificial intelligence to convert these movement data using universal behavior fingerprints into a digital biomarker of disease state. This enables us to predict two different ‘gold-standard’ clinical scores (SCAFI, SARA) that serve as primary clinical endpoints. Crucially, by performing gene expression analysis on each patient their personal Frataxin gene expression levels were poorly, if at all, correlated with their clinical scores – fundamentally failing to establish a link between disease mechanism (dysregulated gene expression) and measures to quantify it in the behavioral phenotype. In contrast, our wearable digital biomarker can accurately predict for each patient their personal FXN gene expression levels, demonstrating the sensitivity of our approach and the importance of FXN levels in FA. Therefore, our data-derived biomarker approach can not only cross-sectionally predict disease and their gene expression levels but also their longitudinal disease trajectory: it is sensitive and accurate enough to detect disease progression with much fewer subjects or shorter time scales than existing primary endpoints. Our work demonstrates that data-derived wearable biomarkers have the potential to substantially reduce clinical trial durations and a first in-human demonstration of reconstructing FXN gene expression levels from behavioral data alone.