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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.

By Kevin Huynh, Florence Lim, Corey Giles, Kaushala S Jayawardana, Agus Salim, Natalie A Mellett, Alexander 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 Saykin, Rebecca Baillie, Xianlin Han, Rima Kaddurah-Daouk, Ralph N Martins, Peter J Meikle

Posted 28 May 2020
medRxiv DOI: 10.1101/2020.05.26.20114215

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 GM3 gangliosides, 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.

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