Turning biomarker data into clinical insight
Using statistics and artificial intelligence to understand disease, risk, and dementia burden across populations.
The problem
What's the challenge, and why is it so hard to solve?
Biomarker measurements generate rich information about neurodegenerative disease, but their value depends on how effectively they can be linked to brain pathology, clinical outcomes, and the diverse factors that shape dementia risk. Robust analytical approaches are needed to translate complex biomarker and population data into clinically meaningful insights that apply across different populations and disease contexts.
Our approach
How are we tackling the challenge?
We integrate data from our own studies with large publicly available datasets, leveraging traditional statistical methods, data mining, and artificial intelligence to translate biomarker measurements into clinical and biological insights. We evaluate how blood-based biomarkers relate to clinical diagnosis, amyloid and tau PET imaging, cognitive decline, and disease progression. We also use biomarkers as indicators of underlying disease processes to investigate how aging, sex, race and ancestry, social determinants of health, environmental exposures, brain injuries, and other health factors contribute to dementia risk, progression, and population-level disease burden.
Why it matters
What difference could this make for patients and society?
Blood-based biomarkers have the potential to transform dementia care by enabling more precise and personalized approaches to diagnosis, prognosis, and treatment. By linking biomarker profiles to clinical outcomes, brain pathology, and diverse risk factors, we can better understand why disease develops and progresses differently across individuals. These insights can support precision medicine strategies that tailor prevention, monitoring, and therapeutic interventions to each person's unique biological and environmental risk profile.
What's next
Once we've solved this, what do we want to explore next?
Next, we aim to translate our findings into clinical practice by turning integrated biomarker, imaging, and clinical data from diverse populations into validated tools that clinicians can use to detect, stage, and monitor disease and to personalize patient care.
Key publications
Where can I read more?
Team
Who's working on this?