Alzheimer’s disease (AD) affects millions globally, yet predicting its onset before symptoms remain a challenge. Metabolic factors linked to AD often overlap with cardiometabolic diseases like obesity, type 2 diabetes, and cardiovascular disease. Our project focus on the role of metabolic factors in AD development, and integrates genetics, proteins and metabolites measured in blood, cognitive tests, brain and body imaging data to develop personalized tools for early AD prediction. We’ll analyze data from large biobanks (UK, Estonian, Norwegian) with real-world hospital registries. Using machine learning, we’ll track metabolic changes from midlife to later years, identifying early AD predictors. This innovative approach shifts the focus from diagnosis to prevention, uncovering metabolic mechanisms and creating clinical tools to forecast AD onset. If successful, this research could transform AD prevention foster a healthier aging population worldwide.