Altered bile acid profile in mild cognitive impairment and Alzheimer's disease: Relationship to neuroimaging and CSF biomarkers

Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid-β deposition. Serum levels of 20 primar...

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Published inAlzheimer's & dementia Vol. 15; no. 2; pp. 232 - 244
Main Authors Nho, Kwangsik, Kueider-Paisley, Alexandra, MahmoudianDehkordi, Siamak, Arnold, Matthias, Risacher, Shannon L., Louie, Gregory, Blach, Colette, Baillie, Rebecca, Han, Xianlin, Kastenmüller, Gabi, Jia, Wei, Xie, Guoxiang, Ahmad, Shahzad, Hankemeier, Thomas, van Duijn, Cornelia M., Trojanowski, John Q., Shaw, Leslie M., Weiner, Michael W., Doraiswamy, P. Murali, Saykin, Andrew J., Kaddurah-Daouk, Rima
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.02.2019
Subjects
Aged
Alzheimer Disease - cerebrospinal fluid
Alzheimer Disease - pathology
Alzheimer's disease
Amyloid beta-Peptides - cerebrospinal fluid
Amyloid-β
Bile acid
Bile Acids and Salts - blood
Bile Acids and Salts - metabolism
Biomarkers - cerebrospinal fluid
Brain glucose metabolism
Cognitive Dysfunction - cerebrospinal fluid
Cognitive Dysfunction - pathology
CSF biomarkers
Female
Fluorodeoxyglucose F18 - metabolism
Gut-liver-brain axis
Humans
Magnetic Resonance Imaging
Male
Metabolomics
MRI
Neuroimaging
PET
Positron-Emission Tomography
Prospective Studies
tau Proteins - cerebrospinal fluid
Metabolomics
Gut-liver-brain axis
MRI
Bile acid
Brain glucose metabolism
Amyloid-β
CSF biomarkers
Alzheimer's disease
PET
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Summary:Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid-β deposition. Serum levels of 20 primary and secondary BA metabolites from the AD Neuroimaging Initiative (n = 1562) were measured using targeted metabolomic profiling. We assessed the association of BAs with the “A/T/N” (amyloid, tau, and neurodegeneration) biomarkers for AD: cerebrospinal fluid (CSF) biomarkers, atrophy (magnetic resonance imaging), and brain glucose metabolism ([18F]FDG PET). Of 23 BAs and relevant calculated ratios after quality control procedures, three BA signatures were associated with CSF Aβ1-42 (“A”) and three with CSF p-tau181 (“T”) (corrected P < .05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t-tau, glucose metabolism, and atrophy (“N”), respectively (corrected P < .05). This is the first study to show serum-based BA metabolites are associated with “A/T/N” AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association.
Bibliography:http://adni.loni.usc.edu/wp‐content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Equal contributors.
As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at
Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database
adni.loni.usc.edu
.
Concept and design: Kaddurah-Daouk led concept and design team that included all co-authors
Biochemical, genomics and medications integration: Kastenmüller, Baillie, Han, Risacher
Drafting of the manuscript: Nho, Kueider-Paisley, Saykin, Kaddurah-Daouk
Data deposition: Alzheimer’s Disease Neuroimaging Initiative (see note)
Supervision: Trojanowski, Shaw, Weiner, Doraiswamy, Saykin, Kastenmüller, Kaddurah-Daouk
The Alzheimer’s Disease Neuroimaging Initiative (ADNI): Data used in the preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Statistical analyses also included: Arnold, Kastenmüller
Author Contributions: Nho, MahmoudianDehkordi, Arnold had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Data management and medication term mapping: Blach
Biochemical interpretation: Baillie, Han, Kaddurah-Daouk
Obtained funding: Kaddurah-Daouk
Equal contributors
Harmonization of methods: Alzheimer’s Disease Metabolomics Consortium (see note)
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Technical, bibliographic research and/or material support: Louie
Critical revision of the manuscript for important intellectual content: Saykin, Doraiswamy, Kaddurah-Daouk
ISSN:1552-5260
1552-5279
DOI:10.1016/j.jalz.2018.08.012