Concordant peripheral lipidome signatures in two large clinical studies of Alzheimer’s disease

Kevin Huynh, Wei Ling Florence Lim, Corey Giles, Kaushala S. Jayawardana, Agus Salim, Natalie A. Mellett, Adam Alexander T. Smith, Gavriel Olshansky, Brian G. Drew, Pratishtha Chatterjee, Ian Martins, Simon M. Laws, Ashley I. Bush, Christopher C. Rowe, Victor L. Villemagne, David Ames, Colin L. Masters, Matthias Arnold, Kwangsik Nho, Andrew J. SaykinRebecca Baillie, Xianlin Han, Rima Kaddurah-Daouk, Ralph N. Martins, Peter J. Meikle*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    69 Citations (Scopus)
    56 Downloads (Pure)


    Changes to lipid metabolism are tightly associated with the onset and pathology of Alzheimer’s disease (AD). Lipids are complex molecules comprising many isomeric and isobaric species, necessitating detailed analysis to enable interpretation of biological significance. Our expanded targeted lipidomics platform (569 species across 32 classes) allows for detailed lipid separation and characterisation. In this study we examined peripheral samples of two cohorts (AIBL, n = 1112 and ADNI, n = 800). We are able to identify concordant peripheral signatures associated with prevalent AD arising from lipid pathways including; ether lipids, sphingolipids (notably GM3 gangliosides) and lipid classes previously associated with cardiometabolic disease (phosphatidylethanolamine and triglycerides). We subsequently identified similar lipid signatures in both cohorts with future disease. Lastly, we developed multivariate lipid models that improved classification and prediction. Our results provide a holistic view between the lipidome and AD using a comprehensive approach, providing targets for further mechanistic investigation.

    Original languageEnglish
    Article number5698
    Pages (from-to)1-11
    Number of pages11
    JournalNature Communications
    Issue number1
    Publication statusPublished - 1 Dec 2020

    Bibliographical note

    Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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