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

Abstract

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 960 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 (4,873 samples from 1,524 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 19% higher rate of AD progression (log-rank test, P = 0.002). 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 GM3 d18:1/20:0 and lysophosphatidylcholine (18:1) showed 19% and 24% higher rates of AD progression, respectively (log-rank test: P = 5.7×10− 9 and 6.2×10− 9). Oleamide, diacylglycerols, plasmalogen lipids, and glycochenodeoxycholic acid were also identified as significantly altered metabolites at baseline in patients with pMCI. Results of a metabolite genome-wide association study showed a low correlation between these metabolites and known genetic risk factors for AD, 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.