Novel mathematical approach to detect early Alzheimer’s Disease
UCL researchers are developing novel mathematical approaches in order to detect Alzheimer’s Disease in its earliest stages, and predict when symptoms will occur.
The predictions made with the new method are more accurate than the current state of the art approaches used. The new technique was tested on patients enrolled in the international DIAN study who developed symptoms of Alzheimer’s disease after entering the study.
Detecting Alzheimer’s Disease early enough to predict when symptoms will occur is particularly difficult as there is a lack of early diagnostic markers. The disease is virtually undetectable for over a decade before the appearance of clinical symptoms, such as memory loss. UCL’s new computational models describe the sequence and timing of events in familial disease progression, including the many years before symptoms are present. These rare, familial forms of disease have a predictable phase before symptoms as they are caused by inherited genetic mutations.
BRC-supported Neil Oxtoby, lead author of the study, said: “Treatments are likely to be more effective if administered early – before the disease damages the brain and the resulting symptoms appear. Our computational models describe the time course and sequence of events in familial Alzheimer’s disease progression up to 20 years before symptoms emerge. We were able to predict when symptom onset will occur to an accuracy of about 1 year”.
The new data-driven modelling methods could make significant changes to the management and treatment of neurodegenerative diseases. This will be explored by further research into applying the methods to the more common form of Alzheimer’s disease, and in other diseases.
This study is an example of how computational methods can be used to understand chronic disease, and how this information can be used to benefit patients and carers.