CRISP – ERC Cognitive Ageing : From Educational Opportunities to Individual Risk Profiles

The five-year project CRISP (2019-2023) investigates cognitive ageing and dementia from a life course and social perspective, with a particular focus on inequalities related to education and gender. By understanding better how to create environments that enable the development of cognitive reserve over the life course, we will be able to make recommendations to policymakers in the domains of education and work. By improving current knowledge on who is at particular risk of cognitive decline, and who will benefit from lifestyle interventions, we will be able to identify vulnerable individuals and ways to support the delaying of cognitive decline. Ideally, we will be able to make recommendations for behavior changes in order to decrease risk of cognitive decline. This project receives funding from the European Research Council (grant agreement no. 803239). Inequalities by gender and socio-economic background, and cognitive functioning later in life Do societal conditions determine to which extent individuals are able to build up cognitive reserve? Since there is no medical cure available to delay cognitive ageing, we need to understand how to create the best possible environments to build up cognitive reserve. We investigate the different opportunities of men and women in terms of education, work and pay, and how they relate to cognitive performance in later life. We also investigate how inequalities in educational opportunities – schooling systems that favor children from higher socio-economic backgrounds – play out their influence on cognitive functioning over the life course. Improving long-term dementia risk prediction and lifestyle interventions with new methods We have some understanding about the high risk groups to develop dementia, and can build on first evidence on short-term benefits of multidomain lifestyle interventions to delay cognitive decline. However, we have very limited generalized knowledge of what intervention works for whom and when. That is why we need to understand more clearly the potential and limits of lifestyle interventions. How do we do this? We use new causal inference frameworks to analyse observational data in order to identify target groups and promising components of future lifestyle interventions. Additionally, we implement recently developed machine learning methods to improve accuracy of risk prediction.