Multi-modal risk scores in cognitive aging and dementia

Non-invasive and low-cost tools for early detection of individuals at high risk for developing Alzheimer’s Disease (AD) are of crucial importance. While genetic studies (GWAS) exist, most focus on European populations, limiting their application to diverse groups. Given the Eurocentric biases in GWASs, PRSs are better at predicting AD risk for European ancestry as opposed to others. This
research addresses this gap by developing a multimodal hazard score (MHS) that incorporates ethnically and genetically diverse populations. Our team established a successful polygenic hazard score (PHS) that predicts AD onset in European cohorts. Higher PHS predicted greater cognitive decline in CN, entorhinal cortex volume loss and predicted conversion from cognitively normal (CN), to mild cognitive impairment (MCI) to AD. The PHS was also associated with neuropathological AD phenotypes and systematically varied AD biomarkers such as amyloid and total tau PET. This suggests its potential as a robust genetic risk
indicator to be included in the MHS. Leveraging data from the largest AD cohorts to date, including ADSP, we will conduct Cox proportional models to develop the MHS combining age, PHS, brain atrophy, biofluid-based data, and clinical outcomes to predict neurocognitive decline trajectory. This score will be able to predict AD progression and identify individuals at high risk of transitioning from cognitive normalcy to mild cognitive impairment (MCI) and eventually AD in a generalizable population. Additionally, we
will also compute power calculations to estimate required clinical trial sample sizes after hypothetical enrichment using the MHS. Finally, we aim to replicate these findings in other diverse cohorts to ensure broader applicability. We believe this MHS surpasses single-modal and European data-driven models in predicting AD for diverse populations. This could pave the way for earlier intervention, more efficient research, and ultimately, improved outcomes for individuals at risk of AD.

Alzheimer’s Disease (AD) disproportionately affects diverse populations, highlighting the need for non-invasive and low-cost tools for early detection. This research aims to develop a multimodal hazard score (MHS) in ethnically and genetically diverse populations. The MHS combines various risk factors, including an individual's genetic profile, neuroimaging, and specific blood markers (biomarkers). This comprehensive approach offers the potential to identify individuals at high risk of transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), ultimately leading to AD. To develop the MHS, we will leverage data from the largest AD cohorts to date, including ADSP. We will then evaluate MHS on its ability to identify individuals with greater risk for converting from CN to MCI and AD on the generalizable population and compute power calculations to estimate required clinical trial sample sizes. This technique could pave the way for earlier intervention, more cost-efficient research, participant burden and ultimately, improved outcomes for individuals at risk of AD.