Abstract
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.
Highlights
Background and SummaryWith the dramatic increase of older adults around the world, Alzheimer’s disease (AD) has become a major public health challenge[1]
Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis
mild cognitive impairment (MCI) is a complex syndrome characterized by memory failures that may be considered as an intermediate stage in the development of AD and is distinct from normal aging[3]
Summary
With the dramatic increase of older adults around the world, Alzheimer’s disease (AD) has become a major public health challenge[1]. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets To better understand this complex etiology, the application of metabolomics for AD research has the potential to monitor molecular alterations associated with disease pathogenesis and progression, as well as to discover candidate diagnostic biomarkers. We apply the medication mapping approach performed previously on ADNI1 to the ADNIGO2 cohort[23] These data are intended to aid in the discovery of metabolic features associated with disease risk, progression, or other clinically and biologically relevant outcomes. We describe both the data collection and tools and resources for data processing, quality control, and analysis
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