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Blood-based metabolic signatures in Alzheimer's disease

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). Ma...

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Autores principales: de Leeuw, Francisca A., Peeters, Carel F.W., Kester, Maartje I., Harms, Amy C., Struys, Eduard A., Hankemeier, Thomas, van Vlijmen, Herman W.T., van der Lee, Sven J., van Duijn, Cornelia M., Scheltens, Philip, Demirkan, Ayşe, van de Wiel, Mark A., van der Flier, Wiesje M., Teunissen, Charlotte E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607205/
https://www.ncbi.nlm.nih.gov/pubmed/28951883
http://dx.doi.org/10.1016/j.dadm.2017.07.006
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author de Leeuw, Francisca A.
Peeters, Carel F.W.
Kester, Maartje I.
Harms, Amy C.
Struys, Eduard A.
Hankemeier, Thomas
van Vlijmen, Herman W.T.
van der Lee, Sven J.
van Duijn, Cornelia M.
Scheltens, Philip
Demirkan, Ayşe
van de Wiel, Mark A.
van der Flier, Wiesje M.
Teunissen, Charlotte E.
author_facet de Leeuw, Francisca A.
Peeters, Carel F.W.
Kester, Maartje I.
Harms, Amy C.
Struys, Eduard A.
Hankemeier, Thomas
van Vlijmen, Herman W.T.
van der Lee, Sven J.
van Duijn, Cornelia M.
Scheltens, Philip
Demirkan, Ayşe
van de Wiel, Mark A.
van der Flier, Wiesje M.
Teunissen, Charlotte E.
author_sort de Leeuw, Francisca A.
collection PubMed
description 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.
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spelling pubmed-56072052017-09-26 Blood-based metabolic signatures in Alzheimer's disease de Leeuw, Francisca A. Peeters, Carel F.W. Kester, Maartje I. Harms, Amy C. Struys, Eduard A. Hankemeier, Thomas van Vlijmen, Herman W.T. van der Lee, Sven J. van Duijn, Cornelia M. Scheltens, Philip Demirkan, Ayşe van de Wiel, Mark A. van der Flier, Wiesje M. Teunissen, Charlotte E. Alzheimers Dement (Amst) Blood-Based Biomarkers 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. Elsevier 2017-09-06 /pmc/articles/PMC5607205/ /pubmed/28951883 http://dx.doi.org/10.1016/j.dadm.2017.07.006 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Blood-Based Biomarkers
de Leeuw, Francisca A.
Peeters, Carel F.W.
Kester, Maartje I.
Harms, Amy C.
Struys, Eduard A.
Hankemeier, Thomas
van Vlijmen, Herman W.T.
van der Lee, Sven J.
van Duijn, Cornelia M.
Scheltens, Philip
Demirkan, Ayşe
van de Wiel, Mark A.
van der Flier, Wiesje M.
Teunissen, Charlotte E.
Blood-based metabolic signatures in Alzheimer's disease
title Blood-based metabolic signatures in Alzheimer's disease
title_full Blood-based metabolic signatures in Alzheimer's disease
title_fullStr Blood-based metabolic signatures in Alzheimer's disease
title_full_unstemmed Blood-based metabolic signatures in Alzheimer's disease
title_short Blood-based metabolic signatures in Alzheimer's disease
title_sort blood-based metabolic signatures in alzheimer's disease
topic Blood-Based Biomarkers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607205/
https://www.ncbi.nlm.nih.gov/pubmed/28951883
http://dx.doi.org/10.1016/j.dadm.2017.07.006
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