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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 Shomorony1,2, 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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