  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.
