Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer’s Disease Neuroimaging Initiative cohort

Takaki Oka, Tokyo University of Agriculture and Technology
Yuki Matsuzawa, Tokyo University of Agriculture and Technology
Momoka Tsuneyoshi, Eisai Co., Ltd.
Yoshitaka Nakamura, Eisai Co., Ltd.
Ken Aoshima, Eisai Co., Ltd.
Hiroshi Tsugawa, Tokyo University of Agriculture and Technology
Michael Weiner, University of California, San Francisco
Paul Aisen, University of California, San Diego
Ronald Petersen, Mayo Clinic
Clifford R. Jack, Mayo Clinic
William Jagust, University of California, Berkeley
John Q. Trojanowki, University of Pennsylvania
Arthur W. Toga, University of Southern California
Laurel Beckett, University of California, Davis
Robert C. Green, Brigham and Women's Hospital
Andrew J. Saykin, Indiana University Bloomington
John Morris, Washington University in St. Louis
Leslie M. Shaw, University of Pennsylvania
Enchi Liu, Janssen Alzheimer Immunotherapy
Tom Montine, University of Washington
Ronald G. Thomas, University of California, San Diego
Michael Donohue, University of California, San Diego
Michael Donohue, University of California, San Diego
Sarah Walter, University of California, San Diego
Devon Gessert, University of California, San Diego
Tamie Sather, University of California, San Diego
Gus Jiminez, University of California, San Diego
Danielle Harvey, University of California, Davis
Matthew Bernstein, Mayo Clinic
Nick Fox, University of London
Paul Thompson, University of South Carolina School of Medicine Greenville
Norbert Schuff, MRI
Charles DeCArli, University of California, Davis
Bret Borowski, Mayo Clinic

Document Type

Article

Publication Date

12-1-2024

Abstract

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10–8 and 4.3 × 10–7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

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