Summary of Study ST002949
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 PR001834. The data can be accessed directly via it's Project DOI: 10.21228/M8NH80 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 | ST002949 |
| Study Title | Serum metabolomics reveals metabolic profile and potential biomarkers of ankylosing spondylitis |
| Study Summary | Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function, quality of life, and work ability in young individuals. Nonetheless, the identification of early radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers remains moderately effective, with unsatisfactory sensitivity and specificity. Hence, it is imperative to identify biomarkers that can facilitate early diagnosis, prognosis, and monitoring of AS. A total of 67 AS patients and 67 healthy controls were recruited to procure plasma samples for the purpose of screening potential biomarkers of AS via untargeted combined with targeted metabolomics approach utlizing UHPLC-QTOF-MS/MS and UHPLC-QQQ-MS/MS. Multivariate pattern recognition and univariate statistical analysis were employed to compare and elucidate the differential metabolites. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites associated with the primary 6 metabolic pathways exhibiting a correlation with AS. Among those, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) value were greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of ROC curve and Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our non-targeted and targeted metabolomics investigations provide new insights into the metabolic profile and potential biomarker candidates of AS. These findings may provide additional diagnostic options for AS and enhance the understanding of the underlying pathophysiology of the condition. |
| Institute | Ningxia Medical University |
| Last Name | Ma |
| First Name | Xueqin |
| Address | 1160 Shenli Street, Yinchuan, Ningxia, 750004, China |
| maxueqin217@126.com | |
| Phone | +86 0951688069 |
| Submit Date | 2023-09-15 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-11-15 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001834 |
| Project DOI: | doi: 10.21228/M8NH80 |
| Project Title: | Serum metabolomics reveals metabolic profile and potential biomarkers of ankylosing spondylitis |
| Project Summary: | Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function, quality of life, and work ability in young individuals. Nonetheless, the identification of early radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers remains moderately effective, with unsatisfactory sensitivity and specificity. Hence, it is imperative to identify biomarkers that can facilitate early diagnosis, prognosis, and monitoring of AS. A total of 67 AS patients and 67 healthy controls were recruited to procure plasma samples for the purpose of screening potential biomarkers of AS via untargeted combined with targeted metabolomics approach utlizing UHPLC-QTOF-MS/MS and UHPLC-QQQ-MS/MS. Multivariate pattern recognition and univariate statistical analysis were employed to compare and elucidate the differential metabolites. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites associated with the primary 6 metabolic pathways exhibiting a correlation with AS. Among those, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) value were greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of ROC curve and Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our non-targeted and targeted metabolomics investigations provide new insights into the metabolic profile and potential biomarker candidates of AS. These findings may provide additional diagnostic options for AS and enhance the understanding of the underlying pathophysiology of the condition. |
| Institute: | Ningxia Medical University |
| Last Name: | Ma |
| First Name: | Xueqin |
| Address: | 1160 Shenli Street, Yinchuan, Ningxia, 750004, China |
| Email: | maxueqin217@126.com |
| Phone: | +86 0951688069 |
