Jorge A. Bahena, United States of America
Washington University School of Medicine PsychiatryAuthor Of 1 Presentation
DETERMINING THE METABOLOMIC AND LIPIDOMIC LANDSCAPE OF ALZHEIMER DISEASE GENETIC SUBTYPES
Abstract
Aims
To interrogate the metabolomic and lipidomic signatures in Alzheimer disease (AD) brain donors including sporadic AD cases, Mendelian mutation (ADAD), and TREM2 risk variant carriers.
Methods
Metabolomic and lipidomic data from parietal brain tissue from donors to the Knight ADRC and DIAN cohorts (ADAD, n=25; TREM2, n= 21; sporadic AD, n=305) and neurologically healthy controls (CO, n=27) were generated using the Metabolon global metabolomics platform. 627 metabolites passed our QC process. Associations between AD subtypes and controls were tested using linear regression models correcting for sex, age, and post-mortem interval. Age was excluded from ADAD models. Benjamini-Hochberg multiple test correction was applied. Pathway analysis was performed with MetaboAnalyst and IMPaLA.
Results
Our analyses identified 138 metabolites associated with disease status (FDR q-value<0.05). For AD brains these include tryptophan betaine (b=-0.55) and N-acetylputrescine (b=-0.14). Metabolites associated both with AD and ADAD brains include ergothioneine (b=-0.22 and -0.26 respectively) and serotonin (b=-0.34 and -0.57 respectively). TREM2 and ADAD brains show association with alpha-tocopherol (b=-0.12 and -0.12). Abundance of beta-citrylglutamate, a component of glutamate metabolism which can be increased by the SSRI fluoxetine, is associated with AD, ADAD, and TREM2 when compared to controls (b=-0.14; -0.22; and -0.29 respectively). Pathway analysis revealed associations with glutamate and serotonin metabolism.
Conclusions
Our findings suggest that AD shows distinct perturbations in various components of metabolism. Investigation of these differentially abundant metabolites may lead to greater insight into the metabolic etiology of AD and/or development of novel biomarkers for disease detection or differentiation between disease types.