Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury

Background Cardiac surgery–associated acute kidney injury (CSA‐AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA‐AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabo...

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Autores principales:	Hao Cui, Songren Shu, Yuan Li, Xin Yan, Xiao Chen, Zujun Chen, Yuxuan Hu, Yuan Chang, Zhenliang Hu, Xin Wang, Jiangping Song
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	acute kidney injury biomarkers cardiac surgery machine learning metabolomics Diseases of the circulatory (Cardiovascular) system RC666-701
Acceso en línea:	https://doaj.org/article/c668b64051d444ba993327462db7c5e4
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spelling	oai:doaj.org-article:c668b64051d444ba993327462db7c5e42021-11-16T10:22:43ZPlasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury10.1161/JAHA.121.0218252047-9980https://doaj.org/article/c668b64051d444ba993327462db7c5e42021-11-01T00:00:00Zhttps://www.ahajournals.org/doi/10.1161/JAHA.121.021825https://doaj.org/toc/2047-9980Background Cardiac surgery–associated acute kidney injury (CSA‐AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA‐AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabolomics‐based biomarkers in patients with CSA‐AKI. Methods and Results A total of 214 individuals (122 patients with acute kidney injury [AKI], 92 patients without AKI as controls) were enrolled in this study. Plasma samples were analyzed by liquid chromatography tandem mass spectrometry using untargeted and targeted metabolomic approaches. Time‐dependent effects of selected metabolites were investigated in an AKI swine model. Multiple machine learning algorithms were used to identify plasma metabolites positively associated with CSA‐AKI. Metabolomic analyses from plasma samples taken within 24 hours following cardiac surgery were useful for distinguishing patients with AKI from controls without AKI. Gluconic acid, fumaric acid, and pseudouridine were significantly upregulated in patients with AKI. A random forest model constructed with selected clinical parameters and metabolites exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879–0.998). In the AKI swine model, plasma levels of the 3 discriminating metabolites increased in a time‐dependent manner (R2, 0.480–0.945). Use of this AKI predictive model was then confirmed in the validation cohort (area under curve, 0.972; 95% CI, 0.947–0.996). The predictive model remained robust when tested in a subset of patients with early‐stage AKI in the validation cohort (area under curve, 0.943; 95% CI, 0.883–1.000). Conclusions High‐resolution metabolomics is sufficiently powerful for developing novel biomarkers. Plasma levels of 3 metabolites were useful for the early identification of CSA‐AKI.Hao CuiSongren ShuYuan LiXin YanXiao ChenZujun ChenYuxuan HuYuan ChangZhenliang HuXin WangJiangping SongWileyarticleacute kidney injurybiomarkerscardiac surgerymachine learningmetabolomicsDiseases of the circulatory (Cardiovascular) systemRC666-701ENJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 10, Iss 22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	acute kidney injury biomarkers cardiac surgery machine learning metabolomics Diseases of the circulatory (Cardiovascular) system RC666-701
spellingShingle	acute kidney injury biomarkers cardiac surgery machine learning metabolomics Diseases of the circulatory (Cardiovascular) system RC666-701 Hao Cui Songren Shu Yuan Li Xin Yan Xiao Chen Zujun Chen Yuxuan Hu Yuan Chang Zhenliang Hu Xin Wang Jiangping Song Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury
description	Background Cardiac surgery–associated acute kidney injury (CSA‐AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA‐AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabolomics‐based biomarkers in patients with CSA‐AKI. Methods and Results A total of 214 individuals (122 patients with acute kidney injury [AKI], 92 patients without AKI as controls) were enrolled in this study. Plasma samples were analyzed by liquid chromatography tandem mass spectrometry using untargeted and targeted metabolomic approaches. Time‐dependent effects of selected metabolites were investigated in an AKI swine model. Multiple machine learning algorithms were used to identify plasma metabolites positively associated with CSA‐AKI. Metabolomic analyses from plasma samples taken within 24 hours following cardiac surgery were useful for distinguishing patients with AKI from controls without AKI. Gluconic acid, fumaric acid, and pseudouridine were significantly upregulated in patients with AKI. A random forest model constructed with selected clinical parameters and metabolites exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879–0.998). In the AKI swine model, plasma levels of the 3 discriminating metabolites increased in a time‐dependent manner (R2, 0.480–0.945). Use of this AKI predictive model was then confirmed in the validation cohort (area under curve, 0.972; 95% CI, 0.947–0.996). The predictive model remained robust when tested in a subset of patients with early‐stage AKI in the validation cohort (area under curve, 0.943; 95% CI, 0.883–1.000). Conclusions High‐resolution metabolomics is sufficiently powerful for developing novel biomarkers. Plasma levels of 3 metabolites were useful for the early identification of CSA‐AKI.
format	article
author	Hao Cui Songren Shu Yuan Li Xin Yan Xiao Chen Zujun Chen Yuxuan Hu Yuan Chang Zhenliang Hu Xin Wang Jiangping Song
author_facet	Hao Cui Songren Shu Yuan Li Xin Yan Xiao Chen Zujun Chen Yuxuan Hu Yuan Chang Zhenliang Hu Xin Wang Jiangping Song
author_sort	Hao Cui
title	Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury
title_short	Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury
title_full	Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury
title_fullStr	Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury
title_full_unstemmed	Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury
title_sort	plasma metabolites–based prediction in cardiac surgery–associated acute kidney injury
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/c668b64051d444ba993327462db7c5e4
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_version_	1718426573238960128

Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury

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