Abstract #0966

Metabolic neuroimaging of ApoE and APP mutational status in mouse models of Alzheimer’s disease

Xiao Gao^1,2,3, Marina Radoul^1,2, Caroline Guglielmetti^1,2, Lydia M. Le Page^1,2, Huihui Li⁴, Yoshitaka Sei⁴, Yadong Huang^4,5,6,7, Ken Nakamura^4,5,6,7, and Myriam M. Chaumeil^1,2,3

¹Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, San Francisco, CA, United States, ²Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ³UCSF/UCB Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, United States, ⁴Gladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, United States, ⁵Department of Neurology, University of California, San Francisco, San Francisco, CA, United States, ⁶Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, United States, ⁷Graduate Program in Biomedical Sciences, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Hyperpolarized MR (Non-Gas), Alzheimer's Disease, Metabolism, Hyperpolarized MR, Proton MRS

Motivation: As metabolic impairment is key in AD, metabolic imaging could potentially improve diagnosis and monitoring of AD.

Goal(s): Our goal is to determine which metabolic imaging approach, or combination of approaches, provide the optimal set of biomarkers for AD.

Approach: We combined three metabolic imaging methods, ¹H MRS, HP ¹³C MRSI and ¹⁸F-FDG PET, with machine learning to characterize the neurometabolic profiles linked to AD-related risk factors, namely ApoE mutation, APP mutation, and sex in AD mouse models.

Results: Combining metabolic neuroimaging and machine learning can help discriminate between AD-related mutational status (APP and ApoE) and provide information of AD-related sexual dimorphism.

Impact: Knowing which metabolic imaging approach(es) is/are optimal to monitor progression in each subset of AD patients, based on sex and mutational status, would improve patient-centric clinical care and potentially create new avenues for assessment of new metabolism-targeting therapies.

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