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Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry

Acute kidney injury (AKI) is defined as a rapid decline in kidney function. The associated syndromes may lead to increased morbidity and mortality, but its early detection remains difficult. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we analyzed the urinary metabolo...

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Autores principales: Saito, Rintaro, Hirayama, Akiyoshi, Akiba, Arisa, Kamei, Yushi, Kato, Yuyu, Ikeda, Satsuki, Kwan, Brian, Pu, Minya, Natarajan, Loki, Shinjo, Hibiki, Akiyama, Shin’ichi, Tomita, Masaru, Soga, Tomoyoshi, Maruyama, Shoichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540909/
https://www.ncbi.nlm.nih.gov/pubmed/34677386
http://dx.doi.org/10.3390/metabo11100671
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author Saito, Rintaro
Hirayama, Akiyoshi
Akiba, Arisa
Kamei, Yushi
Kato, Yuyu
Ikeda, Satsuki
Kwan, Brian
Pu, Minya
Natarajan, Loki
Shinjo, Hibiki
Akiyama, Shin’ichi
Tomita, Masaru
Soga, Tomoyoshi
Maruyama, Shoichi
author_facet Saito, Rintaro
Hirayama, Akiyoshi
Akiba, Arisa
Kamei, Yushi
Kato, Yuyu
Ikeda, Satsuki
Kwan, Brian
Pu, Minya
Natarajan, Loki
Shinjo, Hibiki
Akiyama, Shin’ichi
Tomita, Masaru
Soga, Tomoyoshi
Maruyama, Shoichi
author_sort Saito, Rintaro
collection PubMed
description Acute kidney injury (AKI) is defined as a rapid decline in kidney function. The associated syndromes may lead to increased morbidity and mortality, but its early detection remains difficult. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we analyzed the urinary metabolomic profile of patients admitted to the intensive care unit (ICU) after invasive surgery. Urine samples were collected at six time points: before surgery, at ICU admission and 6, 12, 24 and 48 h after. First, urine samples from 61 initial patients (non-AKI: 23, mild AKI: 24, severe AKI: 14) were measured, followed by the measurement of urine samples from 60 additional patients (non-AKI: 40, mild AKI: 20). Glycine and ethanolamine were decreased in patients with AKI compared with non-AKI patients at 6–24 h in the two groups. The linear statistical model constructed at each time point by machine learning achieved the best performance at 24 h (median AUC, area under the curve: 89%, cross-validated) for the 1st group. When cross-validated between the two groups, the AUC showed the best value of 70% at 12 h. These results identified metabolites and time points that show patterns specific to subjects who develop AKI, paving the way for the development of better biomarkers.
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spelling pubmed-85409092021-10-24 Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry Saito, Rintaro Hirayama, Akiyoshi Akiba, Arisa Kamei, Yushi Kato, Yuyu Ikeda, Satsuki Kwan, Brian Pu, Minya Natarajan, Loki Shinjo, Hibiki Akiyama, Shin’ichi Tomita, Masaru Soga, Tomoyoshi Maruyama, Shoichi Metabolites Article Acute kidney injury (AKI) is defined as a rapid decline in kidney function. The associated syndromes may lead to increased morbidity and mortality, but its early detection remains difficult. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we analyzed the urinary metabolomic profile of patients admitted to the intensive care unit (ICU) after invasive surgery. Urine samples were collected at six time points: before surgery, at ICU admission and 6, 12, 24 and 48 h after. First, urine samples from 61 initial patients (non-AKI: 23, mild AKI: 24, severe AKI: 14) were measured, followed by the measurement of urine samples from 60 additional patients (non-AKI: 40, mild AKI: 20). Glycine and ethanolamine were decreased in patients with AKI compared with non-AKI patients at 6–24 h in the two groups. The linear statistical model constructed at each time point by machine learning achieved the best performance at 24 h (median AUC, area under the curve: 89%, cross-validated) for the 1st group. When cross-validated between the two groups, the AUC showed the best value of 70% at 12 h. These results identified metabolites and time points that show patterns specific to subjects who develop AKI, paving the way for the development of better biomarkers. MDPI 2021-09-30 /pmc/articles/PMC8540909/ /pubmed/34677386 http://dx.doi.org/10.3390/metabo11100671 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Saito, Rintaro
Hirayama, Akiyoshi
Akiba, Arisa
Kamei, Yushi
Kato, Yuyu
Ikeda, Satsuki
Kwan, Brian
Pu, Minya
Natarajan, Loki
Shinjo, Hibiki
Akiyama, Shin’ichi
Tomita, Masaru
Soga, Tomoyoshi
Maruyama, Shoichi
Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry
title Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry
title_full Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry
title_fullStr Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry
title_full_unstemmed Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry
title_short Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry
title_sort urinary metabolome analyses of patients with acute kidney injury using capillary electrophoresis-mass spectrometry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540909/
https://www.ncbi.nlm.nih.gov/pubmed/34677386
http://dx.doi.org/10.3390/metabo11100671
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