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

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • St John-Williams, L; Blach, C; Toledo, JB; Rotroff, DM; Kim, S; Klavins, K; Baillie, R; Han, X; Mahmoudiandehkordi, S; Jack, J; Massaro, TJ; Lucas, JE; Louie, G; Motsinger-Reif, AA; Risacher, SL; Alzheimer’s Disease Neuroimaging Initiative, ; Alzheimer’s Disease Metabolomics Consortium, ; Saykin, AJ; Kastenmüller, G; Arnold, M; Koal, T; Moseley, MA; Mangravite, LM; Peters, MA; Tenenbaum, JD; Thompson, JW; Kaddurah-Daouk, R

Published Date

  • October 17, 2017

Published In

Volume / Issue

  • 4 /

Start / End Page

  • 170140 -

PubMed ID

  • 29039849

Electronic International Standard Serial Number (EISSN)

  • 2052-4463

International Standard Serial Number (ISSN)

  • 2052-4463

Digital Object Identifier (DOI)

  • 10.1038/sdata.2017.140

Language

  • eng