Preventing dementia and Alzheimer disease (AD) is a global priority. Previous single-intervention failures stress the critical need for a new multimodal preventive approach in these complex multifactorial conditions. The Brain Health Toolbox is designed to create a seamless continuum from accurate dementia prediction to effective prevention by i) developing the missing disease models and prediction tools for multimodal prevention; ii) testing them in actual multimodal prevention trials; and iii) bridging the gap between non-pharmacological and pharmacological approaches by designing a combined multimodal prevention trial based on a new European adaptive trial platform. Disease models and prediction tools will be multi-dimensional, i.e. a broad range of risk factors and biomarker types, including novel markers. An innovative machine learning method will be used for pattern identification and risk profiling to highlight most important contributors to an individual’s overall risk level. This is crucial for early identification of individuals with high dementia risk and/or high likelihood of specific brain pathologies, quantifying an individual’s prevention potential, and longitudinal risk and disease monitoring, also beyond trial duration. Three Toolbox test scenarios are considered: use for selecting target populations, assessing heterogeneity of intervention effects, and use as trial outcome. The project is based on a unique set-up aligning several new multimodal lifestyle trials aiming to adapt and test non-pharmacological interventions to different geographic, economic and cultural settings, with two reference libraries (observational - large datasets; and interventional - four recently completed pioneering multimodal lifestyle prevention trials). The Brain Health Toolbox covers the entire continuum from general populations to patients with preclinical/prodromal disease stages, and will provide tools for personalized decision-making for dementia prevention.
Field of science
- /natural sciences/computer and information sciences/artificial intelligence/machine learning
Call for proposal
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