Predicting dementia outcomes using simple, non-invasive assessments: a prospective population-based study

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Background:Around 670,000 people in the UK are currently living with dementia, and this number is expected to double over the next twenty years. Despite many years of research, we still do not have a treatment that prevents or cures this devastating condition. We now understand that the damage causing dementia begins many years before someone develops symptoms, and so it is possible that treatments do not work because the condition is too severe by the time we give them to patients. We therefore need to find a way of identifying people who are currently healthy but are at risk of getting dementia in the future. We ideally need to do this using only the sort of information that is available to GPs, to avoid doing invasive and expensive tests on lots of healthy people. Project aim:In this study I will use data from a large study called UK Biobank, to create a model that uses simple information to predict who is most at risk of developing dementia over a 5-10 year period.Where this research will be performed:This research represents a collaboration of several groups of researchers across several sites at the University of Edinburgh. I will perform the analyses with support, guidance and training from experts in the field.How the predictive model will be created:UK Biobank (UKB) is a very large population-based cohort study of 503,000 middle-aged people. During recruitment participants were extensively evaluated and took brief, electronic thinking tests. In 2014-2015 over 118,000 participants responded to a repeat online memory test, making this the biggest study of repeat cognitive testing ever.The participants in UKB are followed up using routine NHS datasets. When patients are diagnosed with conditions such as dementia either by their GP, in hospital or after they have died, this is recorded in these datasets. The participants in UKB have consented to let UKB access these records so they can learn about their health. Conservative predictions have shown there is likely to be around 4000 dementia cases in the cohort by mid-2017, which would make this by far the largest ever study to create a dementia prediction model. It has also given me relevant research experience and an appreciation of the issues involved when working with data from cohort studies and with healthcare datasets from England, Scotland and Wales.I will apply to access UKB data that includes the information obtained at recruitment, during the repeat online tests and in the routine NHS datasets. I will then investigate which simple characteristics can best predict who is likely to get dementia. These are likely to be things such as age, smoking status, educational level and family history. I will also look at how physical health problems (such as diabetes, heart disease and stroke) might impact on a person's mental health, by seeing whether having one of these conditions increases the risk of getting dementia. I will also use the brief thinking tests that participants took at recruitment and during follow up to see if changes in these can predict who will get dementia before they have obvious symptoms. I will then combine the most predictive characteristics into one model.After creating the model, the next, important stage will be to test it. To do this I will use data from a Scottish study called Generation Scotland (GS). GS has many similarities to UKB in the way participants were recruited and tested. I will also test the model using real-life data from two very large sources of GP data from England and Wales. Why this research matters:We need to change the way we test new dementia treatments to increase the likelihood we find one that works. My goal is to build a prediction tool that can be used to identify people at risk of developing dementia, so they can be invited to participate in trials testing new treatments. If an effective treatment becomes available, doctors could also use this tool to identify who would benefit.