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

Jon B. Toledo, Matthias Arnold, Gabi Kastenmüller, Rui Chang, Rebecca A. Baillie, Xianlin Han, Madhav Thambisetty, Jessica D. Tenenbaum, Karsten Suhre, J. Will Thompson, Lisa St John-Williams, Siamak MahmoudianDehkordi, Daniel M. Rotroff, John R. Jack, Alison Motsinger-Reif, Shannon L. Risacher, Colette Blach, Joseph E. Lucas, Tyler Massaro, Gregory LouieHongjie Zhu, Guido Dallmann, Kristaps Klavins, Therese Koal, Sungeun Kim, Kwangsik Nho, Li Shen, Ramon Casanova, Sudhir Varma, Cristina Legido-Quigley, M. Arthur Moseley, Kuixi Zhu, Marc Y R Henrion, Sven J. van der Lee, Amy C. Harms, Ayse Demirkan, Thomas Hankemeier, Cornelia M. van Duijn, John Q. Trojanowski, Leslie M. Shaw, Andrew Saykin, Michael W. Weiner, P. Murali Doraiswamy, Rima Kaddurah-Daouk

Research output: Contribution to journalArticle

52 Citations (Scopus)

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.

Original languageEnglish (US)
JournalAlzheimer's and Dementia
DOIs
StateAccepted/In press - 2017

Fingerprint

Metabolic Networks and Pathways
Alzheimer Disease
Metabolomics
Metabolic Diseases
Biomarkers
Nonprofit Organizations
Branched Chain Amino Acids
Sphingomyelins
Valine
Drug Discovery
Phosphatidylcholines
Research
Neuroimaging
Ether
Amines
Cerebrospinal Fluid
Disease Progression
Fasting
Industry
Pathology

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

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Geriatrics and Gerontology
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health

Cite this

Toledo, J. B., Arnold, M., Kastenmüller, G., Chang, R., Baillie, R. A., Han, X., ... Kaddurah-Daouk, R. (Accepted/In press). Metabolic network failures in Alzheimer's disease-A biochemical road map. Alzheimer's and Dementia. https://doi.org/10.1016/j.jalz.2017.01.020

Metabolic network failures in Alzheimer's disease-A biochemical road map. / Toledo, Jon B.; Arnold, Matthias; Kastenmüller, Gabi; Chang, Rui; Baillie, Rebecca A.; Han, Xianlin; Thambisetty, Madhav; Tenenbaum, Jessica D.; Suhre, Karsten; Thompson, J. Will; John-Williams, Lisa St; MahmoudianDehkordi, Siamak; Rotroff, Daniel M.; Jack, John R.; Motsinger-Reif, Alison; Risacher, Shannon L.; Blach, Colette; Lucas, Joseph E.; Massaro, Tyler; Louie, Gregory; Zhu, Hongjie; Dallmann, Guido; Klavins, Kristaps; Koal, Therese; Kim, Sungeun; Nho, Kwangsik; Shen, Li; Casanova, Ramon; Varma, Sudhir; Legido-Quigley, Cristina; Moseley, M. Arthur; Zhu, Kuixi; Henrion, Marc Y R; van der Lee, Sven J.; Harms, Amy C.; Demirkan, Ayse; Hankemeier, Thomas; van Duijn, Cornelia M.; Trojanowski, John Q.; Shaw, Leslie M.; Saykin, Andrew; Weiner, Michael W.; Doraiswamy, P. Murali; Kaddurah-Daouk, Rima.

In: Alzheimer's and Dementia, 2017.

Research output: Contribution to journalArticle

Toledo, JB, Arnold, M, Kastenmüller, G, Chang, R, Baillie, RA, Han, X, Thambisetty, M, Tenenbaum, JD, Suhre, K, Thompson, JW, John-Williams, LS, MahmoudianDehkordi, S, Rotroff, DM, Jack, JR, Motsinger-Reif, A, Risacher, SL, Blach, C, Lucas, JE, Massaro, T, Louie, G, Zhu, H, Dallmann, G, Klavins, K, Koal, T, Kim, S, Nho, K, Shen, L, Casanova, R, Varma, S, Legido-Quigley, C, Moseley, MA, Zhu, K, Henrion, MYR, van der Lee, SJ, Harms, AC, Demirkan, A, Hankemeier, T, van Duijn, CM, Trojanowski, JQ, Shaw, LM, Saykin, A, Weiner, MW, Doraiswamy, PM & Kaddurah-Daouk, R 2017, 'Metabolic network failures in Alzheimer's disease-A biochemical road map', Alzheimer's and Dementia. https://doi.org/10.1016/j.jalz.2017.01.020
Toledo, Jon B. ; Arnold, Matthias ; Kastenmüller, Gabi ; Chang, Rui ; Baillie, Rebecca A. ; Han, Xianlin ; Thambisetty, Madhav ; Tenenbaum, Jessica D. ; Suhre, Karsten ; Thompson, J. Will ; John-Williams, Lisa St ; MahmoudianDehkordi, Siamak ; Rotroff, Daniel M. ; Jack, John R. ; Motsinger-Reif, Alison ; Risacher, Shannon L. ; Blach, Colette ; Lucas, Joseph E. ; Massaro, Tyler ; Louie, Gregory ; Zhu, Hongjie ; Dallmann, Guido ; Klavins, Kristaps ; Koal, Therese ; Kim, Sungeun ; Nho, Kwangsik ; Shen, Li ; Casanova, Ramon ; Varma, Sudhir ; Legido-Quigley, Cristina ; Moseley, M. Arthur ; Zhu, Kuixi ; Henrion, Marc Y R ; van der Lee, Sven J. ; Harms, Amy C. ; Demirkan, Ayse ; Hankemeier, Thomas ; van Duijn, Cornelia M. ; Trojanowski, John Q. ; Shaw, Leslie M. ; Saykin, Andrew ; Weiner, Michael W. ; Doraiswamy, P. Murali ; Kaddurah-Daouk, Rima. / Metabolic network failures in Alzheimer's disease-A biochemical road map. In: Alzheimer's and Dementia. 2017.
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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.",
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author = "Toledo, {Jon B.} and Matthias Arnold and Gabi Kastenm{\"u}ller and Rui Chang and Baillie, {Rebecca A.} and Xianlin Han and Madhav Thambisetty and Tenenbaum, {Jessica D.} and Karsten Suhre and Thompson, {J. Will} and John-Williams, {Lisa St} and Siamak MahmoudianDehkordi and Rotroff, {Daniel M.} and Jack, {John R.} and Alison Motsinger-Reif and Risacher, {Shannon L.} and Colette Blach and Lucas, {Joseph E.} and Tyler Massaro and Gregory Louie and Hongjie Zhu and Guido Dallmann and Kristaps Klavins and Therese Koal and Sungeun Kim and Kwangsik Nho and Li Shen and Ramon Casanova and Sudhir Varma and Cristina Legido-Quigley and Moseley, {M. Arthur} and Kuixi Zhu and Henrion, {Marc Y R} and {van der Lee}, {Sven J.} and Harms, {Amy C.} and Ayse Demirkan and Thomas Hankemeier and {van Duijn}, {Cornelia M.} and Trojanowski, {John Q.} and Shaw, {Leslie M.} and Andrew Saykin and Weiner, {Michael W.} and Doraiswamy, {P. Murali} and Rima Kaddurah-Daouk",
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TY - JOUR

T1 - Metabolic network failures in Alzheimer's disease-A biochemical road map

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

AU - Weiner, Michael W.

AU - Doraiswamy, P. Murali

AU - Kaddurah-Daouk, Rima

PY - 2017

Y1 - 2017

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

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JO - Alzheimer's and Dementia

JF - Alzheimer's and Dementia

SN - 1552-5260

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