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Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research

Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this...

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Autores principales: Su, Xiaojuan, Ren, Ruru, Yang, Lingling, Su, Chao, Wang, Yingli, Lu, Jun, Liu, Jing, Zong, Rong, Lu, Fangfang, Wilson, Gidion, Ding, Shuqin, Ma, Xueqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356786/
https://www.ncbi.nlm.nih.gov/pubmed/35942381
http://dx.doi.org/10.1155/2022/7450977
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author Su, Xiaojuan
Ren, Ruru
Yang, Lingling
Su, Chao
Wang, Yingli
Lu, Jun
Liu, Jing
Zong, Rong
Lu, Fangfang
Wilson, Gidion
Ding, Shuqin
Ma, Xueqin
author_facet Su, Xiaojuan
Ren, Ruru
Yang, Lingling
Su, Chao
Wang, Yingli
Lu, Jun
Liu, Jing
Zong, Rong
Lu, Fangfang
Wilson, Gidion
Ding, Shuqin
Ma, Xueqin
author_sort Su, Xiaojuan
collection PubMed
description Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites.
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spelling pubmed-93567862022-08-07 Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research Su, Xiaojuan Ren, Ruru Yang, Lingling Su, Chao Wang, Yingli Lu, Jun Liu, Jing Zong, Rong Lu, Fangfang Wilson, Gidion Ding, Shuqin Ma, Xueqin Evid Based Complement Alternat Med Research Article Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites. Hindawi 2022-07-30 /pmc/articles/PMC9356786/ /pubmed/35942381 http://dx.doi.org/10.1155/2022/7450977 Text en Copyright © 2022 Xiaojuan Su et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Su, Xiaojuan
Ren, Ruru
Yang, Lingling
Su, Chao
Wang, Yingli
Lu, Jun
Liu, Jing
Zong, Rong
Lu, Fangfang
Wilson, Gidion
Ding, Shuqin
Ma, Xueqin
Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research
title Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research
title_full Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research
title_fullStr Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research
title_full_unstemmed Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research
title_short Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research
title_sort serum biomarkers for chronic renal failure screening and mechanistic understanding: a global lc-ms-based metabolomics research
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356786/
https://www.ncbi.nlm.nih.gov/pubmed/35942381
http://dx.doi.org/10.1155/2022/7450977
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