High-Throughput Metabolomics for Discovering Potential Biomarkers and Identifying Metabolic Mechanisms in Aging and Alzheimer’s Disease

Alzheimer’s disease (AD) is an aging-related neurodegenerative disease. We aimed to investigate the metabolic mechanisms of aging and AD and to identify potential biomarkers for the early screening of AD in a natural aging population. To analyze the plasma metabolites related to aging, we conducted an untargeted metabolomics analysis using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry in a two-stage cross-sectional study. Spearman’s correlation analysis and random forest were applied to model the relationship between age and each metabolite. Moreover, a systematic review of metabolomics studies of AD in the PubMed, Cochrane and Embase databases were searched to extract the differential metabolites and altered pathways from original studies. Pathway enrichment analysis was conducted using Mummichog. In total, 669 metabolites were significantly altered with aging, and 12 pathways were enriched and correlated with aging. Three pathways (purine metabolism, arginine and proline metabolism, and the TCA cycle) were shared between aging and AD. Arginine and proline metabolism play a key role in the progression from healthy to mild cognitive impairment and to AD in the natural aging population. Three metabolites, 16-a-hydroxypregnenolone, stearic acid and PC[16:0/22:5(4Z,7Z,10Z,13Z,16Z)] were finally proposed as potential markers of AD in the natural aging population. The underlying mechanism shared between aging and AD and the potential biomarkers for AD diagnosis were proposed based on multistep comparative analysis.