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A Metabolomics study of metabolites associated with the glomerular filtration rate

BACKGROUND: Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilize...

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Autores principales: Peng, Hongquan, Liu, Xun, Ieong, Chiwa Ao, Tou, Tou, Tsai, Tsungyang, Zhu, Haibin, Liu, Zhi, Liu, Peijia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122376/
https://www.ncbi.nlm.nih.gov/pubmed/37085754
http://dx.doi.org/10.1186/s12882-023-03147-9
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author Peng, Hongquan
Liu, Xun
Ieong, Chiwa Ao
Tou, Tou
Tsai, Tsungyang
Zhu, Haibin
Liu, Zhi
Liu, Peijia
author_facet Peng, Hongquan
Liu, Xun
Ieong, Chiwa Ao
Tou, Tou
Tsai, Tsungyang
Zhu, Haibin
Liu, Zhi
Liu, Peijia
author_sort Peng, Hongquan
collection PubMed
description BACKGROUND: Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates. METHODS: An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS). The 145 samples were divided into four groups based on the patient’s measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software. RESULTS: A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function. CONCLUSIONS: This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function.
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spelling pubmed-101223762023-04-23 A Metabolomics study of metabolites associated with the glomerular filtration rate Peng, Hongquan Liu, Xun Ieong, Chiwa Ao Tou, Tou Tsai, Tsungyang Zhu, Haibin Liu, Zhi Liu, Peijia BMC Nephrol Research BACKGROUND: Chronic kidney disease (CKD) is a global public health issue. The diagnosis of CKD would be considerably enhanced by discovering novel biomarkers used to determine the glomerular filtration rate (GFR). Small molecule metabolites related to kidney filtration function that might be utilized as biomarkers to measure GFR more accurately could be found via a metabolomics analysis of blood samples taken from individuals with varied glomerular filtration rates. METHODS: An untargeted metabolomics study of 145 plasma samples was performed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS). The 145 samples were divided into four groups based on the patient’s measured glomerular filtration rates (mGFRs) determined by the iohexol plasma clearance rate. The data were analyzed using random forest analyses and six other unique statistical analyses. Principal component analysis (PCA) was conducted using R software. RESULTS: A large number of metabolites involved in various metabolic pathways changed significantly between groups with different GFRs. These included metabolites involved in tryptophan or pyrimidine metabolism. The top 30 metabolites that best distinguished between the four groups in a random forest plot analysis included 13 amino acids, 9 nucleotides, and 3 carbohydrates. A panel of metabolites (including hydroxyaparagine, pseudouridine, C-glycosyltryptophan, erythronate, N-acetylalanine, and 7-methylguanidine) for estimating GFR was selected for future testing in targeted analyses by combining the candidate lists with the six other statistical analyses. Both hydroxyasparagine and N,N-dimethyl-proline-proline are unique biomarkers shown to be inversely associated with kidney function that have not been reported previously. In contrast, 1,5-anhydroglucitol (1,5-AG) decreases with impaired renal function. CONCLUSIONS: This global untargeted metabolomics study of plasma samples from patients with different degrees of renal function identified potential metabolite biomarkers related to kidney filtration. These novel potential metabolites provide more insight into the underlying pathophysiologic processes that may contribute to the progression of CKD, lead to improvements in the estimation of GFR and provide potential therapeutic targets to improve kidney function. BioMed Central 2023-04-21 /pmc/articles/PMC10122376/ /pubmed/37085754 http://dx.doi.org/10.1186/s12882-023-03147-9 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
Peng, Hongquan
Liu, Xun
Ieong, Chiwa Ao
Tou, Tou
Tsai, Tsungyang
Zhu, Haibin
Liu, Zhi
Liu, Peijia
A Metabolomics study of metabolites associated with the glomerular filtration rate
title A Metabolomics study of metabolites associated with the glomerular filtration rate
title_full A Metabolomics study of metabolites associated with the glomerular filtration rate
title_fullStr A Metabolomics study of metabolites associated with the glomerular filtration rate
title_full_unstemmed A Metabolomics study of metabolites associated with the glomerular filtration rate
title_short A Metabolomics study of metabolites associated with the glomerular filtration rate
title_sort metabolomics study of metabolites associated with the glomerular filtration rate
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122376/
https://www.ncbi.nlm.nih.gov/pubmed/37085754
http://dx.doi.org/10.1186/s12882-023-03147-9
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