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Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema
BACKGROUND: Macular edema (ME) is a major complication of retinal disease with multiple mechanisms involved in its development. This study aimed to investigate the metabolite profile of aqueous humor (AH) in patients with ME of different etiologies and identify potential metabolite biomarkers for ea...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067239/ https://www.ncbi.nlm.nih.gov/pubmed/37004107 http://dx.doi.org/10.1186/s40662-023-00331-8 |
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author | Jiang, Dan Yan, Congcong Ge, Lina Yang, Chun Huang, Ying Chan, Yau Kei Chen, Chonghua Chen, Wei Zhou, Meng Lin, Bing |
author_facet | Jiang, Dan Yan, Congcong Ge, Lina Yang, Chun Huang, Ying Chan, Yau Kei Chen, Chonghua Chen, Wei Zhou, Meng Lin, Bing |
author_sort | Jiang, Dan |
collection | PubMed |
description | BACKGROUND: Macular edema (ME) is a major complication of retinal disease with multiple mechanisms involved in its development. This study aimed to investigate the metabolite profile of aqueous humor (AH) in patients with ME of different etiologies and identify potential metabolite biomarkers for early diagnosis of ME. METHODS: Samples of AH were collected from 60 patients with ME and 20 age- and sex-matched controls and analyzed by liquid chromatography-mass spectrometry (LC/MS)-based metabolomics. A series of univariate and multivariate statistical analyses were performed to identify differential metabolites and enriched metabolite pathways. RESULTS: The metabolic profile of AH differed significantly between ME patients and healthy controls, and differentially expressed metabolites were identified. Pathway analysis revealed that these differentially expressed metabolites are mainly involved in lipid metabolism and amino acid metabolism. Moreover, significant differences were identified in the metabolic composition of AH from patients with ME due to different retinal diseases including age-related macular degeneration (AMD-ME), diabetic retinopathy (DME) and branch retinal vein occlusion (BRVO-ME). In total, 39 and 79 etiology-specific altered metabolites were identified for AMD-ME and DME, respectively. Finally, an AH-derived machine learning-based diagnostic model was developed and successfully validated in the test cohort with an area under the receiver operating characteristic (ROC) curve of 0.79 for AMD-ME, 0.94 for DME and 0.77 for BRVO-ME. CONCLUSIONS: Our study illustrates the potential underlying metabolic basis of AH of different etiologies across ME populations. We also identify AH-derived metabolite biomarkers that may improve the differential diagnosis and treatment stratification of ME patients with different etiologies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40662-023-00331-8. |
format | Online Article Text |
id | pubmed-10067239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100672392023-04-03 Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema Jiang, Dan Yan, Congcong Ge, Lina Yang, Chun Huang, Ying Chan, Yau Kei Chen, Chonghua Chen, Wei Zhou, Meng Lin, Bing Eye Vis (Lond) Research BACKGROUND: Macular edema (ME) is a major complication of retinal disease with multiple mechanisms involved in its development. This study aimed to investigate the metabolite profile of aqueous humor (AH) in patients with ME of different etiologies and identify potential metabolite biomarkers for early diagnosis of ME. METHODS: Samples of AH were collected from 60 patients with ME and 20 age- and sex-matched controls and analyzed by liquid chromatography-mass spectrometry (LC/MS)-based metabolomics. A series of univariate and multivariate statistical analyses were performed to identify differential metabolites and enriched metabolite pathways. RESULTS: The metabolic profile of AH differed significantly between ME patients and healthy controls, and differentially expressed metabolites were identified. Pathway analysis revealed that these differentially expressed metabolites are mainly involved in lipid metabolism and amino acid metabolism. Moreover, significant differences were identified in the metabolic composition of AH from patients with ME due to different retinal diseases including age-related macular degeneration (AMD-ME), diabetic retinopathy (DME) and branch retinal vein occlusion (BRVO-ME). In total, 39 and 79 etiology-specific altered metabolites were identified for AMD-ME and DME, respectively. Finally, an AH-derived machine learning-based diagnostic model was developed and successfully validated in the test cohort with an area under the receiver operating characteristic (ROC) curve of 0.79 for AMD-ME, 0.94 for DME and 0.77 for BRVO-ME. CONCLUSIONS: Our study illustrates the potential underlying metabolic basis of AH of different etiologies across ME populations. We also identify AH-derived metabolite biomarkers that may improve the differential diagnosis and treatment stratification of ME patients with different etiologies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40662-023-00331-8. BioMed Central 2023-04-01 /pmc/articles/PMC10067239/ /pubmed/37004107 http://dx.doi.org/10.1186/s40662-023-00331-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Jiang, Dan Yan, Congcong Ge, Lina Yang, Chun Huang, Ying Chan, Yau Kei Chen, Chonghua Chen, Wei Zhou, Meng Lin, Bing Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema |
title | Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema |
title_full | Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema |
title_fullStr | Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema |
title_full_unstemmed | Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema |
title_short | Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema |
title_sort | metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067239/ https://www.ncbi.nlm.nih.gov/pubmed/37004107 http://dx.doi.org/10.1186/s40662-023-00331-8 |
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