Summary of Study ST003177
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 PR001976. The data can be accessed directly via it's Project DOI: 10.21228/M89J06 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 | ST003177 |
| Study Title | A Longitudinal Study in Rheumatoid Arthritis Unveils Metabolomic Biomarkers Preceding Clinical Onset, Assessing Disease Severity, and Anticipating Treatment Response to csDMARDs |
| Study Summary | Rheumatoid arthritis (RA) is a bundle of systemic inflammatory diseases mainly affecting the joints, complicating the identification of biomarkers for early diagnosis, predicting disease progress and therapeutic outcomes. This study scrutinizes a longitudinal cohort of RA, inclusive of follow-ups, alongside OA, UA and ACPA/RF-RA and healthy controls, aiming to discover plasma metabolic markers that can precede RA onset, assess disease activity, and forecast treatment efficacy. Our investigation revealed substantial metabolic alterations at both the pathway and individual metabolite levels across RA, at-risk or RA and healthy control. The drug response predictive models constructed on critical differential metaboites showed optimal performance. Additionally, our longitudinal data sheds light on the molecular impacts on metabolism of csDMARDs in RA. |
| Institute | West China Hospital of Sichuan University |
| Last Name | Zhu |
| First Name | Chenxi |
| Address | West China Hospital, Sichuan University, 37# Guoxue Xiang, Chengdu, Sichuan, 610041, China. |
| chenxizhu1995@gmail.com | |
| Phone | +8615026603760 |
| Submit Date | 2024-04-12 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2024-05-11 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN005215 | AN005216 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | HILIC | HILIC |
| Chromatography system | SCIEX ExionLC UHPLC | SCIEX ExionLC UHPLC |
| Column | Waters ACQUITY UPLC BEH Amide (100 x 2.1mm,1.7um) | Waters ACQUITY UPLC BEH Amide (100 x 2.1mm,1.7um) |
| MS Type | ESI | ESI |
| MS instrument type | Triple quadrupole | Triple quadrupole |
| MS instrument name | ABI Sciex Triple Quad 5500+ | ABI Sciex Triple Quad 5500+ |
| Ion Mode | POSITIVE | NEGATIVE |
| Units | peak area | Peak area |
MS:
| MS ID: | MS004948 |
| Analysis ID: | AN005215 |
| Instrument Name: | ABI Sciex Triple Quad 5500+ |
| Instrument Type: | Triple quadrupole |
| MS Type: | ESI |
| MS Comments: | Within each batch, normalization was performed by dividing the level of each metabolite by the average value of the first and last QC samples in that batch. Subsequently, a cross-sample total sum correction was conducted for all metabolites. Then, the data was mean-centered and divided by the standard deviation of each variable for standardization. Finally, a log10 transformation was applied to the data. We further removed metabolites with a coefficient of variation (CV) greater than 0.35 and any metabolites with more than 50% missing values within any group (RA, at-risk of RA, and Health). For analyses intolerant to missing data, any missing values are substituted with 1/5 of the minimum positive value of their corresponding variables. |
| Ion Mode: | POSITIVE |
| MS ID: | MS004949 |
| Analysis ID: | AN005216 |
| Instrument Name: | ABI Sciex Triple Quad 5500+ |
| Instrument Type: | Triple quadrupole |
| MS Type: | ESI |
| MS Comments: | Within each batch, normalization was performed by dividing the level of each metabolite by the average value of the first and last QC samples in that batch. Subsequently, a cross-sample total sum correction was conducted for all metabolites. Then, the data was mean-centered and divided by the standard deviation of each variable for standardization. Finally, a log10 transformation was applied to the data. We further removed metabolites with a coefficient of variation (CV) greater than 0.35 and any metabolites with more than 50% missing values within any group (RA, at-risk of RA, and Health). For analyses intolerant to missing data, any missing values are substituted with 1/5 of the minimum positive value of their corresponding variables. |
| Ion Mode: | NEGATIVE |
