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Approved Research

Application Title Development of a disease prediction model aimed to assess the 10-year risk of dementia based on sociodemographic, health, lifestyle, cognitive data, biomarkers and genomics.

Principal Investigator: Dr Liron Jacobson
Approved Research ID: 76708
Approval date: June 27th 2022

Lay summary

Machine learning (ML) is a method of computer programming that learns from data to make predictions about a specified outcome. In this study, we aim to build ML models (from existing data patterns) that can differentiate between healthy ageing individuals and those at risk of dementia. The model will predict the likelihood of developing dementia based on data from sociodemographic information (information about the structure and social details of populations), health and lifestyle questions, brain function (cognitive) assessment, genetics and blood biomarkers (characteristics in the blood which are a sign of disease or illness).

Developing such ML models will initially involve identifying patterns in sociodemographic, health and lifestyle data from UK Biobank, such as education, smoking, BMI (body mass index), diabetes, blood pressure and depression to build a prediction (evaluation) model that can assess the likelihood that someone will developĀ  dementia within 10 years. At the second stage, we will investigate if and how cognitive data (information about memory, processing speed and thinking abilities) can add value to the accuracy of the ML model developed, and lastly, we will do the same with blood biomarkers and genetic data. We intend to use the ML model in our product (mobile device application, Sharp/ Five Lives). This will then be used for validation research for our newly designed cognitive tests that assess memory, processing speed etc. We expect that the project duration will last approximately 12 months.

The number of people suffering fromĀ  dementia is increasing worldwide with no effective treatments available.Therefore, methods to prevent and delay the onset of dementia, as well as an increased understanding of the factors that can increase the risk of developing dementia are crucial to improving the management of dementia cases. Being able to identify individuals who are at an increased risk of dementia will allow for more efficient targeting of available measures that prevent the development of dementia than what is currently possible.

The potential use of digital biomarkers (behavioural or physiological measures collected from digital devices) within prediction tools to detect the early signs of existing and future cognitive decline is also a growing topic of research in the field. Previous studies showed that computerised memory games could detect early signs of cognitive decline. The UK Biobank database also holds cognitive assessment data and we will analyse this data with the aim to improve our prediction ML model.