UCSF

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder, currently affecting more than 7 million Americans aged 65 years and older, with disproportionately higher incidence rates observed among women and Black and Latinx populations. AD is a complex, polygenic, and multifactorial disease that arises through the interaction of genomic susceptibility and exposomic factors, including clinical, lifestyle, and environmental risk exposures experienced across the life course. Recent advances in biomarker research have enabled earlier detection of AD-related processes through plasma and cerebrospinal fluid assays and neuroimaging modalities, supporting preventative intervention during preclinical and prodromal stages. However, the predictive accuracy and generalizability of biomarkers and existing AD risk prediction tools often vary across demographic groups, highlighting the need for analytical validation of these tools in diverse populations. Addressing this gap is critical for developing equitable precision medicine approaches for AD screening and intervention. The present study describes a two-phase investigation aimed at benchmarking genetic, clinical, and environmental measures of AD liability and quantifying the extent to which these risk domains contribute to underlying AD pathology. Collectively, this body of work provides analytical insights into the improvement of risk prediction and assessment strategies in diverse populations while also advancing mechanistic understanding of how genetic, clinical, and environmental factors influence AD pathological processes.