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 - 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
UR - http://www.scopus.com/inward/record.url?scp=85015733333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015733333&partnerID=8YFLogxK
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 -