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Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus
INTRODUCTION: Despite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics app...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7888330/ https://www.ncbi.nlm.nih.gov/pubmed/33593748 http://dx.doi.org/10.1136/bmjdrc-2020-001443 |
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author | Zuo, Jingjing Lan, Yuan Hu, Honglin Hou, Xiangqing Li, Jushuang Wang, Tao Zhang, Hang Zhang, Nana Guo, Chengnan Peng, Fang Zhao, Shuzhen Wei, Yaping Jia, Chaonan Zheng, Chao Mao, Guangyun |
author_facet | Zuo, Jingjing Lan, Yuan Hu, Honglin Hou, Xiangqing Li, Jushuang Wang, Tao Zhang, Hang Zhang, Nana Guo, Chengnan Peng, Fang Zhao, Shuzhen Wei, Yaping Jia, Chaonan Zheng, Chao Mao, Guangyun |
author_sort | Zuo, Jingjing |
collection | PubMed |
description | INTRODUCTION: Despite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently. RESEARCH DESIGN AND METHODS: In this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set. RESULTS: We detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively. CONCLUSIONS: This study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings. |
format | Online Article Text |
id | pubmed-7888330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-78883302021-03-03 Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus Zuo, Jingjing Lan, Yuan Hu, Honglin Hou, Xiangqing Li, Jushuang Wang, Tao Zhang, Hang Zhang, Nana Guo, Chengnan Peng, Fang Zhao, Shuzhen Wei, Yaping Jia, Chaonan Zheng, Chao Mao, Guangyun BMJ Open Diabetes Res Care Genetics/Genomes/Proteomics/Metabolomics INTRODUCTION: Despite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently. RESEARCH DESIGN AND METHODS: In this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set. RESULTS: We detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively. CONCLUSIONS: This study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings. BMJ Publishing Group 2021-02-16 /pmc/articles/PMC7888330/ /pubmed/33593748 http://dx.doi.org/10.1136/bmjdrc-2020-001443 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Genetics/Genomes/Proteomics/Metabolomics Zuo, Jingjing Lan, Yuan Hu, Honglin Hou, Xiangqing Li, Jushuang Wang, Tao Zhang, Hang Zhang, Nana Guo, Chengnan Peng, Fang Zhao, Shuzhen Wei, Yaping Jia, Chaonan Zheng, Chao Mao, Guangyun Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus |
title | Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus |
title_full | Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus |
title_fullStr | Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus |
title_full_unstemmed | Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus |
title_short | Metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus |
title_sort | metabolomics-based multidimensional network biomarkers for diabetic retinopathy identification in patients with type 2 diabetes mellitus |
topic | Genetics/Genomes/Proteomics/Metabolomics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7888330/ https://www.ncbi.nlm.nih.gov/pubmed/33593748 http://dx.doi.org/10.1136/bmjdrc-2020-001443 |
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