Abstract
Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disorder characterised by the progressive death of motor neurons in the brain and spinal cord, leading to fatal paralysis. ALS is relatively rare, with an incidence of about 2 per 100,000 person-years, but the lifetime risk is 1 in 300. Onset is uncommon before age 40. The male-to-female ratio is about 1.3. In up to 20%, genetic factors significantly increase risk, and environmental factors also contribute to pathogenesis.While the motor syndrome and its causes are well studied, in this thesis, I focussed on areas that are poorly understood in an attempt to allow better modelling of the disease, which might allow virtual digital twins for clinical trial design. Signs and symptoms of ALS vary from person to person. Symptoms include walking difficulty, tripping, falling, slurred speech, and muscle cramps. Despite the debilitating motor features of ALS, it is not a pure motor neuron disease. Non-motor symptoms cause significant distress and worsen prognosis and quality of life but have almost no research. ALS is a multisystem disorder and has non-motor symptoms that are poorly defined with poorly understood mechanisms. The distinction between motor and non-motor aetiology is not always precise. Non-motor symptoms include broad and vague symptoms with diverse underlying mechanisms. Two different aspects contribute to the importance of non-motor symptoms: first, the debilitating nature of non-motor symptoms which has a huge effect on a patient’s quality of life and second, the possibility that identification of non-motor symptoms might provide clinicians with the opportunity for early diagnosis of ALS which could yield a longer therapeutic window and play a pivotal role in introducing preventative strategies in ALS. In other words, the non-motor symptoms of ALS are important for treatment modification approaches and may also be considered a diagnostic factor in the early detection of ALS. To research non-motor symptoms in ALS, I created an online survey. I identified patients' most prevalent symptoms.
Although the environmental and genetic risks have been studied extensively, another poorly understood area is the relationship between socioeconomic status and risk. Due to the complex nature of ALS, I also looked into how socioeconomic status can affect the condition. I found a correlation between income and the age at which the disease manifests. I used Mendelian randomization to test whether there are shared genetic backgrounds that can influence ALS risk through socioeconomic status, in order to determine how socioeconomic status affects the disease.
With others, I then used synthetic data methods to create a synthetic placebo population that could mirror the real patient population. We were able to create a viable placebo group using two broad approaches and validated the findings by comparing the synthetic control group with real patients. This work will need to be developed to incorporate the findings from my non-motor symptoms work and socioeconomic studies.
As we understand more about these and other risk and modifying factors in ALS, we will be able to generate more robust virtual patient populations and improve clinical trials.
| Date of Award | 1 Sept 2024 |
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| Original language | English |
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| Supervisor | Ammar Al-Chalabi (Supervisor) & Kallol Ray Chaudhuri (Supervisor) |