Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

• Published in: Scientific data Vol. 4; no. 1; p. 170140
• Main Authors: St John-Williams, Lisa, Blach, Colette, Toledo, Jon B., Rotroff, Daniel M., Kim, Sungeun, Klavins, Kristaps, Baillie, Rebecca, Han, Xianlin, Mahmoudiandehkordi, Siamak, Jack, John, Massaro, Tyler J., Lucas, Joseph E., Louie, Gregory, Motsinger-Reif, Alison A., Risacher, Shannon L., Saykin, Andrew J., Kastenmüller, Gabi, Arnold, Matthias, Koal, Therese, Moseley, M. Arthur, Mangravite, Lara M., Peters, Mette A., Tenenbaum, Jessica D., Thompson, J. Will, Kaddurah-Daouk, Rima
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.10.2017; Nature Publishing Group
• Subjects: 631/114/2402; 631/45/320; 631/45/608; 692/699/375/365/1283; Alzheimer's disease; Brain mapping; Classification; Cognitive ability; Data Descriptor; Data processing; Humanities and Social Sciences; Metabolism; Metabolomics; Molecular modelling; multidisciplinary; Neurodegenerative diseases; Neuroimaging; Pathogenesis; Science
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/sdata.2017.140

Alzheimer’s disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes. Design Type(s) clinical history design • observation design Measurement Type(s) Metabolomics Technology Type(s) mass spectrometry assay Factor Type(s) diagnosis Sample Characteristic(s) Homo sapiens • blood serum Machine-accessible metadata file describing the reported data (ISA-Tab format)