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Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts

Alzheimer’s disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidenc...

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Autores principales: St. John-Williams, Lisa, Mahmoudiandehkordi, Siamak, Arnold, Matthias, Massaro, Tyler, Blach, Colette, Kastenmüller, Gabi, Louie, Gregory, Kueider-Paisley, Alexandra, Han, Xianlin, Baillie, Rebecca, Motsinger-Reif, Alison A., Rotroff, Daniel, Nho, Kwangsik, Saykin, Andrew J., Risacher, Shannon L., Koal, Therese, Moseley, M. Arthur, Tenenbaum, Jessica D., Thompson, J. Will, Kaddurah-Daouk, Rima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797798/
https://www.ncbi.nlm.nih.gov/pubmed/31624257
http://dx.doi.org/10.1038/s41597-019-0181-8
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author St. John-Williams, Lisa
Mahmoudiandehkordi, Siamak
Arnold, Matthias
Massaro, Tyler
Blach, Colette
Kastenmüller, Gabi
Louie, Gregory
Kueider-Paisley, Alexandra
Han, Xianlin
Baillie, Rebecca
Motsinger-Reif, Alison A.
Rotroff, Daniel
Nho, Kwangsik
Saykin, Andrew J.
Risacher, Shannon L.
Koal, Therese
Moseley, M. Arthur
Tenenbaum, Jessica D.
Thompson, J. Will
Kaddurah-Daouk, Rima
author_facet St. John-Williams, Lisa
Mahmoudiandehkordi, Siamak
Arnold, Matthias
Massaro, Tyler
Blach, Colette
Kastenmüller, Gabi
Louie, Gregory
Kueider-Paisley, Alexandra
Han, Xianlin
Baillie, Rebecca
Motsinger-Reif, Alison A.
Rotroff, Daniel
Nho, Kwangsik
Saykin, Andrew J.
Risacher, Shannon L.
Koal, Therese
Moseley, M. Arthur
Tenenbaum, Jessica D.
Thompson, J. Will
Kaddurah-Daouk, Rima
author_sort St. John-Williams, Lisa
collection PubMed
description Alzheimer’s disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI.
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spelling pubmed-67977982019-10-21 Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts St. John-Williams, Lisa Mahmoudiandehkordi, Siamak Arnold, Matthias Massaro, Tyler Blach, Colette Kastenmüller, Gabi Louie, Gregory Kueider-Paisley, Alexandra Han, Xianlin Baillie, Rebecca Motsinger-Reif, Alison A. Rotroff, Daniel Nho, Kwangsik Saykin, Andrew J. Risacher, Shannon L. Koal, Therese Moseley, M. Arthur Tenenbaum, Jessica D. Thompson, J. Will Kaddurah-Daouk, Rima Sci Data Data Descriptor Alzheimer’s disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI. Nature Publishing Group UK 2019-10-17 /pmc/articles/PMC6797798/ /pubmed/31624257 http://dx.doi.org/10.1038/s41597-019-0181-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
St. John-Williams, Lisa
Mahmoudiandehkordi, Siamak
Arnold, Matthias
Massaro, Tyler
Blach, Colette
Kastenmüller, Gabi
Louie, Gregory
Kueider-Paisley, Alexandra
Han, Xianlin
Baillie, Rebecca
Motsinger-Reif, Alison A.
Rotroff, Daniel
Nho, Kwangsik
Saykin, Andrew J.
Risacher, Shannon L.
Koal, Therese
Moseley, M. Arthur
Tenenbaum, Jessica D.
Thompson, J. Will
Kaddurah-Daouk, Rima
Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts
title Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts
title_full Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts
title_fullStr Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts
title_full_unstemmed Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts
title_short Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts
title_sort bile acids targeted metabolomics and medication classification data in the adni1 and adnigo/2 cohorts
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797798/
https://www.ncbi.nlm.nih.gov/pubmed/31624257
http://dx.doi.org/10.1038/s41597-019-0181-8
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