Predictive metabolic networks reveal sex‐ and <i>APOE</i> genotype‐specific metabolic signatures and drivers for precision medicine in Alzheimer's disease
Rui Chang, Eugenia Trushina, Kuixi Zhu, Syed Shujaat Ali Zaidi, Branden M Lau, Alexandra Kueider‐Paisley, Sara Moein, Qianying He, Melissa Alamprese, Barbora Vagnerova, Andrew Tang, Ramachandran Vijayan, Yan‐Yun Liu, Andrew J. Saykin, Roberta Dı́az Brinton, Rima Kaddurah‐Daouk
Abstract
INTRODUCTION: Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine. METHODS: Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort. RESULTS: Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients. DISCUSSION: These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.