TY - JOUR
T1 - Metabolic network failures in Alzheimer's disease
T2 - A biochemical road map
AU - Alzheimer's Disease Neuroimaging Initiative and the Alzheimer Disease Metabolomics Consortium
AU - Toledo, Jon B.
AU - Arnold, Matthias
AU - Kastenmüller, Gabi
AU - Chang, Rui
AU - Baillie, Rebecca A.
AU - Han, Xianlin
AU - Thambisetty, Madhav
AU - Tenenbaum, Jessica D.
AU - Suhre, Karsten
AU - Thompson, J. Will
AU - John-Williams, Lisa St
AU - MahmoudianDehkordi, Siamak
AU - Rotroff, Daniel M.
AU - Jack, John R.
AU - Motsinger-Reif, Alison
AU - Risacher, Shannon L.
AU - Blach, Colette
AU - Lucas, Joseph E.
AU - Massaro, Tyler
AU - Louie, Gregory
AU - Zhu, Hongjie
AU - Dallmann, Guido
AU - Klavins, Kristaps
AU - Koal, Therese
AU - Kim, Sungeun
AU - Nho, Kwangsik
AU - Shen, Li
AU - Casanova, Ramon
AU - Varma, Sudhir
AU - Legido-Quigley, Cristina
AU - Moseley, M. Arthur
AU - Zhu, Kuixi
AU - Henrion, Marc Y.R.
AU - van der Lee, Sven J.
AU - Harms, Amy C.
AU - Demirkan, Ayse
AU - Hankemeier, Thomas
AU - van Duijn, Cornelia M.
AU - Trojanowski, John Q.
AU - Shaw, Leslie M.
AU - Saykin, Andrew J.
AU - Weiner, Michael W.
AU - Doraiswamy, P. Murali
AU - Kaddurah-Daouk, Rima
N1 - Funding Information:
National Institute on Aging (R01AG046171, RF1AG051550, and 3U01AG024904-09S4) supported the Alzheimer Disease Metabolomics Consortium. J.B.T. is supported by National Institute on Aging (R01AG046171, RF1AG051550, and P50 NS053488). A.J.S. is additionally supported by NIA R01 AG19771, NIA P30 AG10133, and NLM R01 LM011360. J.Q.T. is additionally supported by National Institute on Aging (P30 AG10124) in the conduct of ADNI-1 data analyses. M.A. was supported by the Helmholtz cross-program topic “Metabolic Dysfunction.” K.N. was additionally supported by National Library of Medicine (R00 LM011384). L.S. was additionally supported by National Library of Medicine (R01 LM011360) and National Institute of Biomedical Imaging and Bioengineering (R01 EB022574). S.L.R. was additionally supported by National Institute on Aging K01 AG049050, the Alzheimer's Association, the Indiana Clinical and Translational Science Institute, and the Indiana University-IU Health Strategic Neuroscience Research Initiative. K.S. was supported by “Biomedical Research Program” funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation Data collection and sharing for this project was funded by the ADNI (National Institutes of Health grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; Euroimmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The Alzheimer Metabolomics consortium is a part of NIA national initiatives AMP-AD and M2OVE AD.
Publisher Copyright:
© 2017 the Alzheimer's Association
PY - 2017/9
Y1 - 2017/9
N2 - 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.
AB - 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.
KW - Acylcarnitines
KW - Alzheimer's disease
KW - Biochemical networks
KW - Biomarkers
KW - Branched-chain amino acids
KW - Dementia
KW - Metabolism
KW - Metabolomics
KW - Metabonomics
KW - Pharmacometabolomics
KW - Pharmacometabonomics
KW - Phospholipids
KW - Precision medicine
KW - Serum
KW - Sphingomyelins
KW - Systems biology
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U2 - 10.1016/j.jalz.2017.01.020
DO - 10.1016/j.jalz.2017.01.020
M3 - Article
C2 - 28341160
AN - SCOPUS:85015733333
SN - 1552-5260
VL - 13
SP - 965
EP - 984
JO - Alzheimer's and Dementia
JF - Alzheimer's and Dementia
IS - 9
ER -