Cardiovascular and metabolic risk factors (CVMs) pose a significant threat to brain health, contributing to a substantial portion of dementia cases. What if we could detect early signs of brain alterations caused by common health conditions like hypertension and diabetes in midlife, well before cognitive symptoms appear? In this talk, I will describe how leveraged large, harmonized MRI datasets and machine learning to create in-silico severity markers to quantify the impact of CVMs on individual brain MRIs. With these personalized markers, we can potentially identify individuals who are vulnerable to the cognitive effects of CVMs much earlier than current methods allow, opening a critical window for early intervention. These data-driven tools offer critical insights into the link between heart health and brain health, enabling us to identify at-risk individuals and pave the way for precision medicine approaches to dementia prevention.

Teams link: www.win.ox.ac.uk/events/win_seminar_apr_2025