4.7 Article

Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease

Journal

ALZHEIMERS & DEMENTIA
Volume 19, Issue 2, Pages 518-531

Publisher

WILEY
DOI: 10.1002/alz.12675

Keywords

Alzheimer's Disease Neuroimaging Initiative; apolipoprotein E epsilon 4; computational systems biology; late-onset Alzheimer's disease; metabolic biomarkers; metabolic network; metabolomics; precision medicine; sex-specific metabolic changes

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This study constructed sex- and APOE-specific metabolic networks based on changes in metabolites and identified patient-specific biomarkers predictive of disease state. The findings provide critical insights for personalized medicine for late-onset Alzheimer's disease.
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 epsilon 4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE epsilon 4 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.

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