Summary of Study ST002535
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001631. The data can be accessed directly via it's Project DOI: 10.21228/M8W43G This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002535 |
| Study Title | Relationships between the gut microbiome and cognitive impairment in residents of long-term aged care. |
| Study Summary | Ageing-associated cognitive decline affects more than half of those in long-term residential aged care. Emerging evidence suggests that gut microbiome-host interactions influence the effects of modifiable risk factors. We investigated the relationship between gut microbiome characteristics and severity of cognitive impairment (CI) in 159 residents of long-term aged care. Severe CI was associated with a significantly increased abundance of proinflammatory bacterial species, including Methanobrevibacter smithii and Alistipes finegoldii, and decreased relative abundance of beneficial bacterial clades. Severe CI was associated with increased microbial capacity for methanogenesis, and reduced capacity for synthesis of short-chain fatty acids, neurotransmitters glutamate and gamma-aminobutyric acid, and amino acids required for neuro-protective lysosomal activity. These relationships were independent of age, sex, antibiotic exposure, and diet. Our findings implicate multiple gut microbiome-brain pathways in ageing-associated cognitive decline, including inflammation, neurotransmission, and autophagy, and highlight the potential to predict and prevent cognitive decline through microbiome-targeted strategies. |
| Institute | South Australian Health and Medical Research Institute |
| Last Name | Shoubridge |
| First Name | Andrew |
| Address | North Terrace, Adelaide, South Australia, 5000, Australia |
| andrew.shoubridge@sahmri.com | |
| Phone | +61405041977 |
| Submit Date | 2023-03-26 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-04-04 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN004170 |
|---|---|
| Analysis type | MS |
| Chromatography type | HILIC |
| Chromatography system | Agilent 1200 |
| Column | Merck SeQuant ZIC-HILIC (150 x 2.1mm,5um) |
| MS Type | ESI |
| MS instrument type | QTOF |
| MS instrument name | Agilent 6545 QTOF |
| Ion Mode | NEGATIVE |
| Units | ng/ml |
MS:
| MS ID: | MS003917 |
| Analysis ID: | AN004170 |
| Instrument Name: | Agilent 6545 QTOF |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Negative mode LC-MS data were collected in centroid mode with a scan range of 50 to 1700 mass-to-charge ratio (m/z) and an acquisition rate of 1.2 spectra/s. Samples were analyzed in the same analytical batch and randomized with a quality control every five samples. Authentic standards were also run to generate the library for targeted analysis. Level 1 metabolite identification [according to the Metabolite Standard Initiative] was based on matching accurate mass, retention time, and tandem MS (MS/MS) spectra to the 550 authentic standards in the MA in-house library. Metabolite abundance based on area under the curve (AUC) was obtained using Agilent Masshunter Quantitative Analysis B 0.7.00. Statistical analysis was performed applying the web-based platform MetaboAnalyst applying no missing value imputation, normalization to median peak area, and no scaling or transformation. RAW_FILE_NAME=AS_01.mzML to AS_35.mzML refer to metabolite detection of short-chain fatty acids, and RAW_FILE_NAME=AS_001.mzML to AS_035.mzML refer the polar metabolites. |
| Ion Mode: | NEGATIVE |
