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Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease
The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by u...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947328/ https://www.ncbi.nlm.nih.gov/pubmed/33718374 http://dx.doi.org/10.3389/fcell.2021.637489 |
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author | Chang, Rui Zhu, Ying Xu, Jing Chen, Lin Su, Guannan Kijlstra, Aize Yang, Peizeng |
author_facet | Chang, Rui Zhu, Ying Xu, Jing Chen, Lin Su, Guannan Kijlstra, Aize Yang, Peizeng |
author_sort | Chang, Rui |
collection | PubMed |
description | The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Through univariate and multivariate statistical analysis, we found 9 differential metabolites when comparing VKH patients with healthy controls, and 26 differential metabolites were identified when comparing active VKH patients with inactive VKH patients. Pathway enrichment analysis showed that glycine, serine and threonine metabolism, and arginine and proline metabolism were significantly altered in VKH versus healthy controls. Lysine degradation and biotin metabolism pathways were significantly altered in active VKH versus inactive VKH. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the combination of acetylglycine and gamma-glutamylalanine could differentiate VKH from healthy controls with an area under the curve (AUC) of 0.808. A combination of ureidopropionic acid and 5′-phosphoribosyl-5-amino-4-imidazolecarboxamide (AICAR) had an excellent AUC of 0.958 for distinguishing active VKH from inactive VKH. In summary, this study identified abnormal metabolites in urine of patients with VKH disease. Further studies are needed to confirm whether these metabolites are specific for this disease. |
format | Online Article Text |
id | pubmed-7947328 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79473282021-03-12 Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease Chang, Rui Zhu, Ying Xu, Jing Chen, Lin Su, Guannan Kijlstra, Aize Yang, Peizeng Front Cell Dev Biol Cell and Developmental Biology The diagnosis of Vogt-Koyanagi-Harada (VKH) disease is mainly based on a complex clinical manifestation while it lacks objective laboratory biomarkers. To explore the potential molecular biomarkers for diagnosis and disease activity in VKH, we performed an untargeted urine metabolomics analysis by ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS). Through univariate and multivariate statistical analysis, we found 9 differential metabolites when comparing VKH patients with healthy controls, and 26 differential metabolites were identified when comparing active VKH patients with inactive VKH patients. Pathway enrichment analysis showed that glycine, serine and threonine metabolism, and arginine and proline metabolism were significantly altered in VKH versus healthy controls. Lysine degradation and biotin metabolism pathways were significantly altered in active VKH versus inactive VKH. Furthermore, the receiver operating characteristic (ROC) curve analysis revealed that the combination of acetylglycine and gamma-glutamylalanine could differentiate VKH from healthy controls with an area under the curve (AUC) of 0.808. A combination of ureidopropionic acid and 5′-phosphoribosyl-5-amino-4-imidazolecarboxamide (AICAR) had an excellent AUC of 0.958 for distinguishing active VKH from inactive VKH. In summary, this study identified abnormal metabolites in urine of patients with VKH disease. Further studies are needed to confirm whether these metabolites are specific for this disease. Frontiers Media S.A. 2021-02-25 /pmc/articles/PMC7947328/ /pubmed/33718374 http://dx.doi.org/10.3389/fcell.2021.637489 Text en Copyright © 2021 Chang, Zhu, Xu, Chen, Su, Kijlstra and Yang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Chang, Rui Zhu, Ying Xu, Jing Chen, Lin Su, Guannan Kijlstra, Aize Yang, Peizeng Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease |
title | Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease |
title_full | Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease |
title_fullStr | Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease |
title_full_unstemmed | Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease |
title_short | Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease |
title_sort | identification of urine metabolic biomarkers for vogt-koyanagi-harada disease |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947328/ https://www.ncbi.nlm.nih.gov/pubmed/33718374 http://dx.doi.org/10.3389/fcell.2021.637489 |
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