Summary of Study ST002921
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 PR001815. The data can be accessed directly via it's Project DOI: 10.21228/M83Q7Q 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 | ST002921 |
| Study Title | Metabolomic Characteristics of Nontuberculous Mycobacterial Pulmonary Disease |
| Study Summary | While the burden of nontuberculous mycobacterial pulmonary disease (NTM-PD) continues to increase, knowledge of biomarkers for NTM-PD remains insufficient. Furthermore, metabolic changes in NTM-PD have not yet been investigated. The identification of specific metabolites and associated metabolic pathways unique to NTM-PD will provide a deeper understanding of its pathogenesis. In this study, we aimed to discover specific metabolic biomarkers for NTM-PD using a metabolomics approach. In this study, we underwent untargeted metabolomic profiling using a liquid chromatography system coupled with the quadrupole-orbitrap mass spectrometer to analyze serum samples from patients with NTM-PD (n = 50), patients with non-NTM bronchiectasis (n = 50), and HC subjects (n = 60). To validate the results, an additional 86 serum samples for each group were analyzed using the same analytical methods. We identified several NTM-PD significant metabolites that differentiate patients with NTM-PD from healthy individuals. The machine learning-based classification model demonstrated the proficiency of the selected metabolic features in distinguishing between patients with NTM-PD and healthy individuals. These findings may contribute to a better understanding of the pathogenesis of NTM-PD and provide insights for the development of novel treatment approaches. |
| Institute | Seoul National University College of Medicine and Hospital |
| Last Name | Jungeun |
| First Name | Kim |
| Address | 101 Daehak-ro, Jongno-gu, Seoul, Korea |
| jeunk@snu.ac.kr | |
| Phone | +821026965910 |
| Submit Date | 2023-10-05 |
| Num Groups | 3 |
| Total Subjects | 418 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2024-01-31 |
| Release Version | 1 |
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Sample Preparation:
| Sampleprep ID: | SP003040 |
| Sampleprep Summary: | The serum samples (500 μL) were stored at −80°C until further analysis. Before metabolomic analysis, the frozen samples were thawed on ice and vortexed. Then, each serum (80 μL) was deproteinized by adding 350 μL prechilled acetonitrile/methanol (1:1, v/v), followed by shaking for 5 min and centrifugation at 18,341 × g for 10 min. Supernatant (400 μL) was transferred to a new tube and then further centrifuged. The resulting supernatant (350 μL) was diluted with equal volume of distilled water, and 150 μL of the final solution was transferred to an autosampler vial for analysis. A pooled quality control (PQC) sample was created by combining 50 μL aliquots from all serum samples. The PQC sample was randomly placed within the sequence after preparation, alongside the analysis samples. |
| Processing Storage Conditions: | -80℃ |
| Extract Storage: | On ice |
