TY - JOUR
T1 - Blood-based metabolic signatures in Alzheimer's disease
AU - de Leeuw, Francisca A.
AU - Peeters, Carel F.W.
AU - Kester, Maartje I.
AU - Harms, Amy C.
AU - Struys, Eduard A.
AU - Hankemeier, Thomas
AU - van Vlijmen, Herman W.T.
AU - van der Lee, Sven J.
AU - van Duijn, Cornelia M.
AU - Scheltens, Philip
AU - Demirkan, Ayşe
AU - van de Wiel, Mark A.
AU - van der Flier, Wiesje M.
AU - Teunissen, Charlotte E.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Amino acids
KW - Biomarkers
KW - Graphical modeling
KW - Metabolomics
KW - Oxidative stress
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U2 - https://doi.org/10.1016/j.dadm.2017.07.006
DO - https://doi.org/10.1016/j.dadm.2017.07.006
M3 - Article
C2 - 28951883
SN - 2352-8729
VL - 8
SP - 196
EP - 207
JO - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
JF - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
ER -
