With the aging of the global population, the prevalence of dementia is rising, placing a significant and growing burden on healthcare systems and society. Despite more than a decade of intensive biomarker research, the field still lacks robust tools to accurately predict who will develop dementia, how the disease will progress, and when clinical symptoms will emerge. This highlights a critical need for innovative, non-invasive diagnostic strategies across the disease continuum. Developing predictive and mechanistically informative biomarkers could support earlier diagnosis, improve risk stratification, and enable more effective intervention and management.
The UK Biobank offers a uniquely rich dataset, including biomarker assays, cognitive assessments, proteomics and metabolomics data, genetic profiles, and longitudinal follow-up, providing an unparalleled opportunity to explore disease trajectories from midlife to dementia onset. Leveraging this resource using systems-based AI/ML approaches will allow for the discovery of actionable, non-invasive biomarkers to advance precision medicine approaches for dementia prevention.
Our objectives is to identify predictive blood metabolite and protein biomarkers and investigate their causal associations with biological processes involved in dementia to improve early detection, deepen our understanding of disease mechanisms, and ultimately contribute to personalized and timely treatment strategies.
