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Alzheimers Dement. 2017 Sep;13(9):965-984. doi: 10.1016/j.jalz.2017.01.020. Epub 2017 Mar 22.

Metabolic network failures in Alzheimer's disease: A biochemical road map.

Author information

1
Department of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Houston Methodist Hospital, Houston, TX, USA. Electronic address: jbtoledoatucha@houstonmethodist.org.
2
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
3
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany.
4
Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
5
Rosa & Co LLC, San Carlos, CA, USA.
6
Sanford Burnham Prebys Medical Discovery Institute, Orlando, FL, USA.
7
Clinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
8
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
9
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Physiology and Biophysics, Weill Cornell Medical College, Qatar, Doha, Qatar.
10
Duke Proteomics and Metabolomics Shared Resource, Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
11
Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, NC, USA.
12
Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; The Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA.
13
Duke Molecular Physiology Institute, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
14
Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA.
15
Department of Psychiatry, Duke University, Durham, NC, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, USA.
16
BIOCRATES Life Sciences AG, Innsbruck, Austria.
17
IPS, Faculty of Life Sciences and Medicine, King's College London, London.
18
Department of Epidemiology, ErasmusMC, Rotterdam, The Netherlands.
19
Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
20
Department of Epidemiology, ErasmusMC, Rotterdam, The Netherlands; Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
21
Department of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.
22
Department of Radiology, Center for Imaging of Neurodegenerative Diseases, San Francisco VA Medical Center/University of California San Francisco, San Francisco, CA, USA.
23
Department of Psychiatry, Duke University, Durham, NC, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University Medical Center, Durham, NC, USA. Electronic address: kaddu001@mc.duke.edu.

Abstract

INTRODUCTION:

The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

METHODS:

Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.

RESULTS:

Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.

DISCUSSION:

Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

KEYWORDS:

Acylcarnitines; Alzheimer's disease; Biochemical networks; Biomarkers; Branched-chain amino acids; Dementia; Metabolism; Metabolomics; Metabonomics; Pharmacometabolomics; Pharmacometabonomics; Phospholipids; Precision medicine; Serum; Sphingomyelins; Systems biology

PMID:
28341160
PMCID:
PMC5866045
DOI:
10.1016/j.jalz.2017.01.020
[Indexed for MEDLINE]
Free PMC Article

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