Dementia is a progressive condition that affects nearly 10 million new people worldwide every year, with around 900,000 cases in the UK alone. Despite the significant public health burden, existing predictive models are only moderately accurate due to their reliance on a limited number of associated factors, many of which may not be causal. These models often miss important risk factors, leading to limited understanding of dementia’s complex mechanisms and suboptimal treatment options. Our project aims to overcome these limitations by utilising large-scale Biobank data to systematically investigate all potential risk factors, avoiding the selective focus of past studies. By identifying causal factors, we will provide clearer guidance for prevention, allowing for targeted interventions that focus on the factors with the greatest impact. This approach is crucial because current prevention strategies attempt to modify multiple risk factors (e.g., exercise, cognition, diet, sleep), leading to diluted public health efforts. Causal models will allow for more focused interventions that can be communicated clearly to at-risk individuals. We will also address a significant gap in research by stratifying our prediction models by ethnicity, which will help reduce healthcare inequalities and improve personalised dementia prevention strategies. Few studies have examined how dementia risk factors differ across ethnic groups, and our project will fill this gap, creating more equitable and effective models for diverse populations. The growing availability of biobank data makes this an achievable and timely effort, offering the potential for more accurate identification of at-risk groups and earlier preventive interventions that are personalised and culturally appropriate.
