Prevention of brain decline:general and individual factors influencing benefit from nutrition, physical activity and lifestyle interventions

The main goal of the PrecisePrevent project is to enhance understanding of how brain aging can be mitigated through accessible, low-cost, non-pharmacological interventions. With substantial individual differences in response to interventions, a key question is: who benefits most, and from which interventions? The project will examine the relationship between lifestyle factors—such as nutrition, sleep, physical activity, and cognitive engagement—and brain health in aging individuals. By combining large-scale longitudinal brain imaging studies with a mega-analysis of seven experimental brain health interventions, we aim to identify both general and individual factors that influence the effectiveness of interventions. These findings will be implemented in a digital, personalized intervention that spans four countries, allowing us to test how lifestyle changes impact brain aging and cognitive function. We will develop individual-level predictors and intervention targets by analyzing genetic and non-genetic factors, and create an approach that enables individuals to select personalized targets and assess the predicted effects on their brain health. To address the challenge of sustaining lifestyle changes, we will collaborate with stakeholders to design a platform that optimizes cost-benefit efficiency through machine learning. The intervention programs will be delivered through a validated mobile health platform. This will allow us to compare the effectiveness of personalized versus general approaches and contribute to developing a scalable, low-threshold intervention framework suitable for widespread use across Europe. The ultimate goal is to generate societal benefits by aggregating modest individual improvements in brain health.

The main aim of PrecisePrevent is to contribute knowledge about how brain aging can be ameliorated through low-cost, easily accessible non-pharmacological means. There are large individual differences in response to interventions, and a crucial question regards who benefits the most from what? We will test the associations between lifestyle factors, including nutrition, sleep, physical and cognitive activity, and brain health in aging. We will leverage retrospective large-scale longitudinal brain imaging studies combined with mega-analysis of seven experimental brain health interventions. General and specific factors for individuals and interventions will be systematically mapped. The results will be implemented in a digital personalized intervention run across four countries. This allows us directly to test how much brain aging and cognitive function can be affected by lifestyle changes, and which variables that contribute to promote or restrict the effects. We will systematically estimate how much benefits can be optimized using genetic and non-genetic factors to generate individual-level predictors and intervention targets. As lifestyle changes are difficult to sustain, we will, working with stakeholders and target-groups, develop a personalized intervention which allows participants to select their own targets and evaluate predicted effects on brain health. Machine learning will be used to optimize the cost-benefit balance. The personalized intervention programs will be delivered and assessed through an interactive mobileHealth platform, which we already have preliminary validated. This enables us to determine the increase in benefits from personalized vs. general intervention approaches. The aim is to develop a framework for a scalable low-threshold intervention feasible at a pan-European level. The rationale is that aggregation of modest individual benefits will result in substantial societal advantages.