Blood-based metabolic signatures in Alzheimer's disease

Abstract

Introduction: Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers. Methods: We included 127 AD patients and 121 controls with CSF-biomarker-confirmed diagnosis (cut-off tau/A$\beta_{42}$: 0.52). Mass spectrometry platforms determined the concentrations of 53 amine, 22 organic acid, 120 lipid, and 40 oxidative stress compounds. Multiple signatures were assessed: differential expression (nested linear models), classification (logistic regression), and regulatory (network extraction). Results: Twenty-six metabolites were differentially expressed. Metabolites improved the classification performance of clinical variables from 74% to 79%. Network models identified 5 hubs of metabolic dysregulation: Tyrosine, glycylglycine, glutamine, lysophosphatic acid C18:2 and platelet activating factor C16:0. The metabolite network for APOE $\epsilon$4 negative AD patients was less cohesive compared to the network for APOE $\epsilon$4 positive AD patients. Discussion: Multiple signatures point to various promising peripheral markers for further validation. The network differences in AD patients according to APOE genotype may reflect different pathways to AD.