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.

Document Type

Article

Publication Date

2024

Notes/Citation Information

© The Author(s) 2024

Digital Object Identifier (DOI)

https://doi.org/10.1038/s41598-024-56837-1

Funding Information

This work was supported by JSPS KAKENHI (21K18216 to Hiroshi T.), the National Cancer Center Research and Development Fund (2020-A-9, Hiroshi T.), AMED Japan Program for Infectious Diseases Research and Infrastructure (21wm0325036h0001, Hiroshi T.), AMED Brain/MINDS (JP15dm0207001, Hiroshi T.), JST National Bioscience Database Center (JPMJND2305, Hiroshi T.), and JST ERATO Grant (JPMJER2101 to Hiroshi T.).

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