| First Author | Hunsberger HC | Year | 2020 |
| Journal | Sci Rep | Volume | 10 |
| Issue | 1 | Pages | 12171 |
| PubMed ID | 32699218 | Mgi Jnum | J:294065 |
| Mgi Id | MGI:6453116 | Doi | 10.1038/s41598-020-68739-z |
| Citation | Hunsberger HC, et al. (2020) Divergence in the metabolome between natural aging and Alzheimer's disease. Sci Rep 10(1):12171 |
| abstractText | Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder and one of the leading causes of death in the United States. Although amyloid plaques and fibrillary tangles are hallmarks of AD, research suggests that pathology associated with AD often begins 20 or more years before symptoms appear. Therefore, it is essential to identify early-stage biomarkers in those at risk for AD and age-related cognitive decline (ARCD) in order to develop preventative treatments. Here, we used an untargeted metabolomics analysis to define system-level alterations following cognitive decline in aged and APP/PS1 (AD) mice. At 6, 12, and 24 months of age, both control (Ctrl) and AD mice were tested in a 3-shock contextual fear conditioning (CFC) paradigm to assess memory decline. AD mice exhibited memory deficits across age and these memory deficits were also seen in naturally aged mice. Prefrontal cortex (PFC), hippocampus (HPC), and spleen were then collected and analyzed for metabolomic alterations. A number of significant pathways were altered between Ctrl and AD mice and naturally aged mice. By identifying systems-level alterations following ARCD and AD, these data could provide insights into disease mechanisms and advance the development of biomarker panels. |
