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Combining two large clinical cohorts (AIBL and ADNI) to identify multiple lipid metabolic pathways in prevalent and incident Alzheimer’s disease

Authors :
Xianlin Han
Christopher C. Rowe
Agus Salim
Simon M. Laws
Ashley I. Bush
Pratishtha Chatterjee
Gavriel Olshansky
Rima Kaddurah-Daouk
Brian G. Drew
Wei Ling Florence Lim
Ralph N. Martins
David Ames
Andrew J. Saykin
Ian Martins
Rebecca Baillie
Victor L. Villemagne
Alexander Ian Smith
Natalie A. Mellett
Peter J. Meikle
Matthias Arnold
Kevin Huynh
Kaushala S. Jayawardana
Colin L. Masters
Kwangsik Nho
Corey Giles
Publication Year :
2020
Publisher :
Cold Spring Harbor Laboratory, 2020.

Abstract

Changes to lipid metabolism are tightly associated with the onset and pathology of Alzheimer’s disease (AD). Lipids are complex molecules comprising of many isomeric and isobaric species, necessitating detailed analysis to enable interpretation of biological significance. Our expanded targeted lipidomics platform (569 lipid species across 32 lipid (sub)classes) allows for detailed isomeric and isobaric lipid separation. We applied the methodology to examine plasma samples from the Australian Imaging, Biomarkers and Lifestyle flagship study of aging (AIBL, n = 1112) and serum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n = 800) studies. Cross sectional analysis using both cohorts identified concordant unique peripheral signatures associated with AD. Specific pathways include; sphingolipids, including GM3gangliosides, where their acyl composition drove the major associations, and lipids previously associated with dysfunctional lipid metabolism in cardiometabolic disease including the phosphatidylethanolamine and triglyceride classes. Infomation derived from improved isomeric seperation highlighted pathway-specific changes with ether lipids including plasmalogens implicating perixosmal dysfunction in disease pathology. Longitudinal analysis revealed similar lipid signitures in both AIBL and ADNI cohorts with future disease onset. We utilised the two independent studies to train and validate multivariate lipid models that significantly improved disease classification and prediction. Together our results provide a holistic view of the lipidome and its relationship with AD using a comprehensive lipidomics approach, providing targets for further mechanistic investigation.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....6918d81d13f01128f2785a65ed65029b