Collaborative: Predicting Progression in Multiple Sclerosis Using Wearable Technologies, Retinal Imaging and Blood Biomarkers

MRC’s Collaborative awards provide students with experience of collaborative research with a non academic partner, enabling the student to spend a period of time with the non-academic partner (usually no less than three months over the lifetime of the PhD).

Background

Multiple sclerosis (MS), the commonest non-traumatic cause of neurological disability in people under 40 years of age, affects approximately 1 in 350 people in Scotland. Globally, around 2.8 million people live with MS.¹ Most individuals (85-90%) present with relapsing-remitting MS (RRMS), while 10-15% have primary progressive MS (PPMS). Despite advances in disease-modifying therapies (DMTs), many people with RRMS ultimately develop a progressive phase of disease associated with accumulating disability, called secondary progressive MS (SPMS). 

Disability progression in individuals with well-controlled RRMS and SPMS is largely driven by Progression Independent of Relapse Activity (PIRA).² The recent emergence of treatments that target PIRA has created an urgent need for tools to identify progression at its earliest stages. The ability to identify individuals entering a progressive disease trajectory before conventional clinical confirmation would enable more personalised treatment decisions, including earlier initiation of therapies targeting progression. Such approaches align with the principles of precision medicine by matching DMT to delay or prevent an individual's predicted disease course.

Our laboratory-based work has identified a novel gait signature, consisting of a subtle irreversible foot drop combined with exercise-induced reversible foot drop (indicating fatigability). In a longitudinal follow up of a gait study (n=30),³ these measures predicted PIRA up to four years before clinical confirmation. These findings are currently being evaluated in an independent cohort (n=15) at the Cleveland Clinic, USA.

Early identification of PIRA using laboratory-based methods relies on repeated specialist assessments, which may be difficult to access for people living in rural and remote areas or for those facing mobility, socioeconomic, or logistical barriers to regular clinic attendance. Digital monitoring technologies offer an opportunity to reduce these inequalities by enabling objective, frequent assessment of neurological function in home and community settings. This could support more equitable access to advanced disease monitoring and timely treatment decisions regardless of geography.

To address this challenge, we are working with Celestra Health Systems to further develop and validate wearable digital biomarkers of neurological disability, using instrumented smart insoles capable of monitoring gait in real-world settings, while assessing subtle gait changes in a laboratory setting. The smart insole technology also forms a key component of the planned FIXin-MS (Fatigability Induced by eXercise in MS) clinical trial. 

This project will leverage the landmark FutureMS cohort,⁴ which recruited 440 people with newly diagnosed MS from across Scotland and collected longitudinal MRI, optical coherence tomography (OCT), blood biomarkers and detailed clinical phenotyping over the first five years following diagnosis. Of the 323 participants that completed the 5 year follow up, 141 reside in Lothian and surrounding area. A second wave of follow-up is now underway, approximately 10 years after diagnosis, creating a unique opportunity to identify a single or a combination of biomarkers that predict the emergence of PIRA.

Aims

1. Perform detailed laboratory-based 3-dimensional gait analysis (gold standard) in a subset of the FutureMS cohort (n=30) every six months over two years at the Anne Rowling Clinic to determine the reproducibility of the predictive value of subtle foot drop with fatigability for future PIRA.
2. Validate Celestra smart insoles for gait fatigability measurements (n=30) against gold-standard gait assessment using the Motek system and Vicon three-dimensional motion capture at the Edinburgh Centre for Robotics.
3. Determine whether longitudinal gait metrics derived from smart insoles (Celestra Health systems) can predict future PIRA (n=30, manageable number based on our longitudinal 3-dimensioonal gait data) through weekly remote monitoring in real-world settings.
4. Determine whether combining digital gait biomarkers with OCT-derived measures of neuroaxonal injury, standard MRI changes and blood biomarkers of tissue damage improves prediction of future PIRA compared with individual biomarker modalities alone.

References: 

1. Walton C, King R, Rechtman L, Kaye W, Leray E, Marrie RA, et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition. Mult Scler. 2020 Dec;26(14):1816-1821. doi: 10.1177/1352458520970841. Epub 2020 Nov 11. PMID: 33174475; PMCID: PMC7720355.
2. Müller J, Sharmin S, Lorscheider J, Ozakbas S, Karabudak R, Horakova D, et al. Standardized Definition of Progression Independent of Relapse Activity (PIRA) in Relapsing-Remitting Multiple Sclerosis. JAMA Neurol. 2025 Jun 1;82(6):614-625. doi: 10.1001/jamaneurol.2025.0495. PMID: 40227706; PMCID: PMC11997854.
3. Andreopoulou G, Mercer TH, Enriquez JG, Justin M, MacLeod N, Harrison E, Mahad DJ, van der Linden ML. Exercise-induced changes in gait kinematics in multiple sclerosis with minimal neurological disability. Mult Scler Relat Disord. 2021 Jan;47:102630. doi: 10.1016/j.msard.2020.102630. Epub 2020 Nov 19. PMID: 33232909.
4. Kearns PKA, Martin SJ, Chang J, Meijboom R, York EN, Chen Y, et al. FutureMS cohort profile: a Scottish multicentre inception cohort study of relapsing-remitting multiple sclerosis. BMJ Open. 2022 Jun 29;12(6):e058506. doi: 10.1136/bmjopen-2021-058506. PMID: 35768080; PMCID: PMC9244691.