Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by the loss of cortical, brain stem, and spinal motor neurons which lead to muscle weakening, motor function loss, and cognitive impairment.1 ALS is a progressive neurodegenerative condition, and patients will initially present with symptoms of motor neuron loss affecting one body region. However as motor neuron loss progresses there will not only be progression of weakness in that body region, but also the spread of weakness to eventually involve the entire body and respiratory muscles, leading to death within 5 years of symptom onset.2 The gold standard for diagnosis is the clinical examination, and disease progression in clinical trials is monitored using a patient-reported symptom questionnaire called the Revised ALS Functional Rating Scale (ALSFRS-r).1 Both of these measures, however, are subjective and variable even in the hands of experienced clinicians due to both ‘true’ biological variability (fluctuation in symptoms, variable disease course, etc.) and the imprecise nature of the measures themselves.1–3 Additionally, investigative results (e.g. electromyography (EMG) and molecular diagnostic techniques for certain biomarkers) alone are unable to adequately diagnose ALS, as definitive ALS diagnosis requires the presence of the characteristic clinical findings mentioned.2 Finally, clinical diagnostic techniques are further limited by the need for visible motor symptoms, which can occur multiple months or potentially years following initial disease onset.4
One emerging avenue of investigation that intends to address the shortcomings of clinical examinations and the ALSFRS-r for ALS diagnosis and disease monitoring, is the use of wearable digital tools that quantitatively measure motor outcomes in patients.5 Past research has shown the viability of using such tools to track physical characteristics in speech, gait, ocular movement, respiratory and autonomic dysfunction, for the diagnosis and monitoring of a range of neurological diseases such as ataxias, muscular dystrophies, and myopathies.5 Further, preliminary studies from the Hengen lab at Washington University in St. Louis (WashU) (unpublished) suggest that sub-second capacitive touch tracking of the behavior and motion patterns of mice genetically predisposed to ALS can predict disease symptom on onset and progression more accurately and quickly than a visual examination.
There is therefore a need to develop a digital tool capable of diagnosing and monitoring ALS onset and progression in humans more accurately and quickly than the standard ALSFRS-r. The target population of such a tool would be individuals known to carry genes that predispose them to ALS, therefore increasing the likelihood that they develop ALS at some point in their lives. Approximately 5-10% of ALS cases are due to this familial form of the disease, and even more individuals who are genetically predisposed but never develop symptoms.6 The target audience of this tool would primarily be these genetically disposed individuals, as well as the clinicians involved in the diagnosis, management, and treatment of ALS. This tool would be used continuously at home and at work, and therefore would not be limited to a controlled laboratory environment. This continuous, long-term use would both account for the diurnal and day-to-day variably of ALS symptoms as well as allow for the analysis of large amounts of user data, increasing the accuracy of the analysis.7
The client chosen for this project is Ravi Chopra, MD, PhD who is currently a Postdoctoral Research Fellow in the Hengen Lab as well as a Clinical Fellow in the Neuromuscular Division in the WashU Neurology Department. In the Hengen lab, Dr. Chopra explores the hypothesis that abnormal neural dynamics in the brain and spinal cord of ALS mouse models may predict, and even drive, the neurodegenerative process. His work is, therefore, closely aligned with the goals mentioned above and he represents an exceptionally appropriate client for this project.
References
1. Calvo, A. C. et al. Amyotrophic Lateral Sclerosis: A Focus on Disease Progression. BioMed Res. Int. 2014, 925101 (2014).
2. Wijesekera, L. C. & Nigel Leigh, P. Amyotrophic lateral sclerosis. Orphanet J. Rare Dis. 4, 3 (2009).
3. Turner, M. R. Diagnosing ALS: the Gold Coast criteria and the role of EMG. Pract. Neurol. practneurol-2021-003256 (2022) doi:10.1136/practneurol-2021-003256.
4. Benatar, M., Wuu, J., Andersen, P. M., Lombardi, V. & Malaspina, A. Neurofilament light: A candidate biomarker of presymptomatic amyotrophic lateral sclerosis and phenoconversion. Ann. Neurol. 84, 130–139 (2018).
5. Torri, F. et al. The use of digital tools in rare neurological diseases towards a new care model: a narrative review. Neurol. Sci. 45, 4657–4668 (2024).
6. Katsuno, M., Tanaka, F. & Sobue, G. Perspectives on molecular targeted therapies and clinical trials for neurodegenerative diseases. J. Neurol. Neurosurg. Psychiatry 83, 329–335 (2012).
7. Gupta, A. S., Patel, S., Premasiri, A. & Vieira, F. At-home wearables and machine learning sensitively capture disease progression in amyotrophic lateral sclerosis. Nat. Commun. 14, 5080 (2023).