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INTRODUCTION: Identification of blood-based metabolic changes might provide early and easy-to-obtain biomarkers. METHODS: We included 127 Alzheimer's disease (AD) patients and 121 control subjects with cerebrospinal fluid biomarker-confirmed diagnosis (cutoff tau/amyloid β peptide 42: 0.52). Mass spectrometry platforms determined the concentrations of 53 amine compounds, 22 organic acid compounds, 120 lipid compounds, 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 five hubs of metabolic dysregulation: tyrosine, glycylglycine, glutamine, lysophosphatic acid C18:2, and platelet-activating factor C16:0. The metabolite network for apolipoprotein E (APOE) ε4 negative AD patients was less cohesive compared with the network for APOE ε4 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.

Original publication

DOI

10.1016/j.dadm.2017.07.006

Type

Journal article

Journal

Alzheimers Dement (Amst)

Publication Date

2017

Volume

8

Pages

196 - 207

Keywords

Alzheimer's disease, Amino acids, Biomarkers, Graphical modeling, Metabolomics, Oxidative stress