Dataset Information

MetaboLights MTBLS72 - GNPS Plasma Lipidomics for the Identification of Anticedent Memory Impairment

ABSTRACT: This data set was downloaded from MetaboLights (http://www.ebi.ac.uk/metabolights/) accession number MTBLS72 Abstract:Alzheimer’s disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050. There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-beta levels, structural and functional magnetic resonance imaging and the recent use of brain amyloid imaging or inflammaging, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer's disease with the required sensitivity and specificity.

Herein, we describe our lipidomic approach to detecting preclinical Alzheimer’s disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer’s disease within a 2–3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer's disease.

In this study, a combination of targeted and untargeted metabolomics/lipidomics approach was used in conjunction with statistical analysis for identification of a bio-signature of pre-clinical Alzheimer’s disease.

INSTRUMENT(S): UPLC-TQ-S (Biocrates analysis)

ORGANISM(S): Homo Sapiens

SUBMITTER: Federoff

PROVIDER: MSV000081035 | GNPS | Fri Apr 28 13:46:00 BST 2017

REPOSITORIES: GNPS

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Project description:Winter2017 - Brain Energy Metabolism with PPP This model is described in the article: Mathematical analysis of the influence of brain metabolism on the BOLD signal in Alzheimer's disease Felix Winter1,2, Catrin Bludszuweit-Philipp1 and Olaf Wolkenhauer2,3 Journal of Cerebral Blood Flow & Metabolism Abstract: Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a standard clinical tool for the detection of brain activation. In Alzheimer’s disease (AD), task-related and resting state fMRI have been used to detect brain dysfunction. It has been shown that the shape of the BOLD response is affected in early AD. To correctly interpret these changes, the mechanisms responsible for the observed behaviour need to be known. The parameters of the canonical hemodynamic response function (HRF) commonly used in the analysis of fMRI data have no direct biological interpretation and cannot be used to answer this question. We here present a model that allows relating AD-specific changes in the BOLD shape to changes in the underlying energy metabolism. According to our findings, the classic view that differences in the BOLD shape are only attributed to changes in strength and duration of the stimulus does not hold. Instead, peak height, peak timing and full width at half maximum are sensitive to changes in the reaction rate of several metabolic reactions. Our systems-theoretic approach allows the use of patient-specific clinical data to predict dementia- driven changes in the HRF, which can be used to improve the results of fMRI analyses in AD patients. This model is hosted on BioModels Database and identified by: BIOMD0000000627. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

MetaboLights MTBLS72 - GNPS Plasma Lipidomics for the Identification of Anticedent Memory Impairment

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