Abstract #3332

Multimodal Models Provide Earlier Prediction (10 Years Prior to Diagnosis) of Dementia and Cognitive Decline and Personalized Actionability for Risk Mitigation for At-Risk Individuals

Natalie M Schenker-Ahmed¹, Ilan Shomorony^1,2, Jian Wu¹, Alex Graff¹, Naisha Shah¹, Peter Garst¹, Nafisa Bulsara¹, Krisztina Marosi¹, Dmitry Tkach¹, Lei Huang¹, Axel Bernal¹, Jason Deckman¹, Hyun-Kyung Chung¹, Wayne Delport¹, David S Karow¹, and Christine Leon Swisher¹

¹Human Longevity, Inc., San Diego, CA, United States, ²Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Current approaches for predicting an individual’s risk of developing dementia rely primarily on single modality data and/or single biomarkers. Here we evaluate the utilization of non-invasive MR imaging and genetics for early detection and prediction of cognitive decline and dementia. We demonstrate superior performance of our multi-measurement, multimodal approach. Moreover, our approach performs as well or better than invasive amyloid PET. We further show a method that identifies modifiable factors upon which an individual can act to mitigate their risk with the long-term goal of empowering high-risk individuals with personalized action plans earlier when the disease progression can be slowed.

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