| Autor: |
Chang, Rui, Trushina, Eugenia, Zhu, Kuixi, Zaidi, Syed Shujaat Ali, Lau, Branden M., Kueider‐Paisley, Alexandra, Moein, Sara, He, Qianying, Alamprese, Melissa L., Vagnerova, Barbora, Tang, Andrew, Vijayan, Ramachandran, Liu, Yanyun, Saykin, Andrew J., Brinton, Roberta D., Kaddurah‐Daouk, Rima |
| Zdroj: |
Alzheimer's & Dementia: The Journal of the Alzheimer's Association; Feb2023, Vol. 19 Issue 2, p518-531, 14p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
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