The Preclinical and Prodromal ALS session focused on early diagnosis of MND and the use of biomarkers to reveal early signs of the disease. A key approach to this type of research is to use a lot of data, with information collected from people over time. Crucially, this includes information gathered from people before symptoms appeared. This can be a powerful tool when looking for changes.
Jesper Storgaard talked about his work using Denmark’s national health registers. He specifically looked at blood cholesterol and markers of muscle breakdown in people in Denmark diagnosed with MND between 2009 and 2022, a total of 2,870 people. He and his team found high levels of cholesterol in people with MND up to 10 years before they first visited their doctor with symptoms. They also found changes in the amount of muscle breakdown markers between one and three years before that point. These results suggest changes in cholesterol may be an early feature of MND and might be useful in identifying people at greater risk of developing MND.
Dr Johnathan Cooper-Knock presented work using data from the UK Biobank. He and his team searched data from 281 people with MND, collected up to 5,000 days (over 13 years) before diagnosis. They used artificial intelligence (AI) to identify patterns in certain proteins and compare these from people with MND to those from people with other conditions such as Alzheimer’s and asthma, and healthy people. They found their programme could predict a future diagnosis of MND through measuring levels of these proteins. They found that levels of these proteins increase 1000 days before symptom onset. They’re currently testing their programme with more data with the hope that, in the future, they’ll be able to use the programme to screen people at greater risk of developing MND, such as people with inherited changes in their genes.
Dr Michael Benatar discussed his research, which uses pre-symptomatic familial ALS (pre-fALS) study data. This is a study which collects information from people who don’t have MND but are at greater risk of developing the disease because of an inherited gene change. He and his team have used this data to identify a set of 19 proteins which could be used in the future to predict which people with inherited gene changes will go on to develop MND, and when they might develop symptoms. This panel of proteins enabled more accurate and reliable predictions of disease than using neurofilament light chain (NfL) alone. These results need validating and replicating in other sets of data, but they may accelerate progress to clinical trials for prevention in people at risk of inherited MND.
