Summary of Study ST003124
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 PR001942. The data can be accessed directly via it's Project DOI: 10.21228/M8PX4X 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 | ST003124 |
| Study Title | Serum metabolites in inherited retinal degenerations |
| Study Summary | The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti’s crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in the external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study. |
| Institute | National Taiwan University |
| Department | Department of Chemistry |
| Laboratory | Cheng-Chih Hsu's lab |
| Last Name | Chung |
| First Name | Hsin-Hsiang |
| Address | No. 1, Sec. 4, Roosevelt Rd. |
| hhchung@ntu.edu.tw | |
| Phone | +886-2-3366-1681 |
| Submit Date | 2024-03-11 |
| Total Subjects | 155 |
| Num Males | 90 |
| Num Females | 65 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzXML |
| Analysis Type Detail | LC-MS |
| Release Date | 2024-03-17 |
| Release Version | 1 |
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Subject:
| Subject ID: | SU003241 |
| Subject Type: | Human |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
