| First Author | Chang CW | Year | 2024 |
| Journal | ACS Chem Neurosci | Volume | 15 |
| Issue | 3 | Pages | 527-538 |
| PubMed ID | 38269400 | Mgi Jnum | J:350732 |
| Mgi Id | MGI:7664309 | Doi | 10.1021/acschemneuro.3c00587 |
| Citation | Chang CW, et al. (2024) Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer's Disease Using Mass Spectrometry-Based Metabolomics. ACS Chem Neurosci 15(3):527-538 |
| abstractText | Hair emerged as a biospecimen for long-term investigation of endogenous metabolic perturbations, reflecting the chemical composition circulating in the blood over the past months. Despite its potential, the use of human hair for metabolomics in Alzheimer's disease (AD) research remains limited. Here, we performed both untargeted and targeted metabolomic approaches to profile the key metabolic pathways in the hair of 5xFAD mice, a widely used AD mouse model. Furthermore, we applied the discovered metabolites to human subjects. Hair samples were collected from 6-month-old 5xFAD mice, a stage marked by widespread accumulation of amyloid plaques in the brain, followed by sample preparation and high-resolution mass spectrometry analysis. Forty-five discriminatory metabolites were discovered in the hair of 6-month-old 5xFAD mice compared to wild-type control mice. Enrichment analysis revealed three key metabolic pathways: arachidonic acid metabolism, sphingolipid metabolism, and alanine, aspartate, and glutamate metabolism. Among these pathways, six metabolites demonstrated significant differences in the hair of 2-month-old 5xFAD mice, a stage prior to the onset of amyloid plaque deposition. These findings suggest their potential involvement in the early stages of AD pathogenesis. When evaluating 45 discriminatory metabolites for distinguishing patients with AD from nondemented controls, a combination of l-valine and arachidonic acid significantly differentiated these two groups, achieving a 0.88 area under the curve. Taken together, these findings highlight the potential of hair metabolomics in identifying disease-specific metabolic alterations and developing biomarkers for improving disease detection and monitoring. |
