Blood-based metabolic signatures in Alzheimer's disease

Francisca A. de Leeuw; Carel F.W. Peeters; Maartje I. Kester; Amy C. Harms; Eduard A. Struys; Thomas Hankemeier; Herman W.T. van Vlijmen; Sven J. van der Lee; Cornelia M. van Duijn; Philip Scheltens; Ayşe Demirkan; Mark A. van de Wiel; Wiesje M. van der Flier; Charlotte E. Teunissen

doi:10.1016/j.dadm.2017.07.006

Blood-based metabolic signatures in Alzheimer's disease

Francisca A. de Leeuw^*, Carel F.W. Peeters, Maartje I. Kester, Amy C. Harms, Eduard A. Struys, Thomas Hankemeier, Herman W.T. van Vlijmen, Sven J. van der Lee, Cornelia M. van Duijn, Philip Scheltens, Ayşe Demirkan, Mark A. van de Wiel, Wiesje M. van der Flier, Charlotte E. Teunissen

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

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 language	English
Pages (from-to)	196-207
Number of pages	12
Journal	Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
Volume	8
Early online date	6 Sept 2017
DOIs	https://doi.org/10.1016/j.dadm.2017.07.006
Publication status	Published - 2017

Funding

This research was supported by Janssen Pharmaceuticals Stellar Initiative: Stellar Neurodegeneration Collaboration Project, Call 2, No. 3 (An Integrated MetaboloMic, Epidemiologic and genetic approach to DIscover clinically relevant biomarkers for Alzheimers Disease: IMMEDIAD). Research of the VUmc Alzheimer center is part of the neurodegeneration research program of Amsterdam Neuroscience. The VUmc Alzheimer center is supported by Alzheimer Nederland and Stichting VUmc fonds. The clinical database structure was developed with funding from Stichting Dioraphte. F.A.d.L is appointed at the NWO-FCB project NUDAD (project number 057-14-004).

Funders	Funder number
Stichting Retina Fonds
Alzheimer Nederland
Janssen Pharmaceuticals
Horizon 2020 Framework Programme	667375, 645740
Not added	057-14-004

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.dadm.2017.07.006

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Cite this

de Leeuw, F. A., Peeters, C. F. W., Kester, M. I., Harms, A. C., Struys, E. A., Hankemeier, T., van Vlijmen, H. W. T., van der Lee, S. J., van Duijn, C. M., Scheltens, P., Demirkan, A., van de Wiel, M. A., van der Flier, W. M., & Teunissen, C. E. (2017). Blood-based metabolic signatures in Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 8, 196-207. https://doi.org/10.1016/j.dadm.2017.07.006

@article{36c070cc30f5424696bd73ee51c9e528,

title = "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 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.",

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

author = "\{de Leeuw\}, \{Francisca A.\} and Peeters, \{Carel F.W.\} and Kester, \{Maartje I.\} and Harms, \{Amy C.\} and Struys, \{Eduard A.\} and Thomas Hankemeier and \{van Vlijmen\}, \{Herman W.T.\} and \{van der Lee\}, \{Sven J.\} and \{van Duijn\}, \{Cornelia M.\} and Philip Scheltens and Ay{\c s}e Demirkan and \{van de Wiel\}, \{Mark A.\} and \{van der Flier\}, \{Wiesje M.\} and Teunissen, \{Charlotte E.\}",

year = "2017",

doi = "10.1016/j.dadm.2017.07.006",

language = "English",

volume = "8",

pages = "196--207",

journal = "Alzheimer's \& Dementia: Diagnosis, Assessment \& Disease Monitoring",

issn = "2352-8729",

publisher = "Wiley",

}

de Leeuw, FA, Peeters, CFW, Kester, MI, Harms, AC, Struys, EA, Hankemeier, T, van Vlijmen, HWT, van der Lee, SJ, van Duijn, CM, Scheltens, P, Demirkan, A, van de Wiel, MA, van der Flier, WM & Teunissen, CE 2017, 'Blood-based metabolic signatures in Alzheimer's disease', Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 8, pp. 196-207. https://doi.org/10.1016/j.dadm.2017.07.006

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

Y1 - 2017

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 - 10.1016/j.dadm.2017.07.006

DO - 10.1016/j.dadm.2017.07.006

M3 - Article

AN - SCOPUS:85029499078

SN - 2352-8729

VL - 8

SP - 196

EP - 207

JO - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

JF - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

ER -

Blood-based metabolic signatures in Alzheimer's disease

Abstract

Funding

Keywords

UN SDGs

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Persistent URL (handle)

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