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Metabolites
Volume 11
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10.3390/metabo11100671
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Article

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

by
Rintaro Saito
1,*,
Akiyoshi Hirayama
1,
Arisa Akiba
1,
Yushi Kamei
1,
Yuyu Kato
1,
Satsuki Ikeda
1,
Brian Kwan
2,
Minya Pu
2,
Loki Natarajan
2,
Hibiki Shinjo
3,
Shin’ichi Akiyama
3,
Masaru Tomita
1,
Tomoyoshi Soga
1 and
Shoichi Maruyama
3
1
Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Japan
2
Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093, USA
3
Department of Nephrology, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan
*
Author to whom correspondence should be addressed.
Metabolites 2021, 11(10), 671; https://doi.org/10.3390/metabo11100671
Submission received: 6 September 2021 / Revised: 28 September 2021 / Accepted: 28 September 2021 / Published: 30 September 2021
(This article belongs to the Section Advances in Metabolomics)
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Abstract

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.
Keywords: AKI; capillary electrophoresis-mass spectrometry (CE-MS); biomarker; urine AKI; capillary electrophoresis-mass spectrometry (CE-MS); biomarker; urine

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MDPI and ACS Style

Saito, R.; Hirayama, A.; Akiba, A.; Kamei, Y.; Kato, Y.; Ikeda, S.; Kwan, B.; Pu, M.; Natarajan, L.; Shinjo, H.; et al. Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry. Metabolites 2021, 11, 671. https://doi.org/10.3390/metabo11100671

AMA Style

Saito R, Hirayama A, Akiba A, Kamei Y, Kato Y, Ikeda S, Kwan B, Pu M, Natarajan L, Shinjo H, et al. Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry. Metabolites. 2021; 11(10):671. https://doi.org/10.3390/metabo11100671

Chicago/Turabian Style

Saito, Rintaro, Akiyoshi Hirayama, Arisa Akiba, Yushi Kamei, Yuyu Kato, Satsuki Ikeda, Brian Kwan, Minya Pu, Loki Natarajan, Hibiki Shinjo, and et al. 2021. "Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry" Metabolites 11, no. 10: 671. https://doi.org/10.3390/metabo11100671

APA Style

Saito, R., Hirayama, A., Akiba, A., Kamei, Y., Kato, Y., Ikeda, S., Kwan, B., Pu, M., Natarajan, L., Shinjo, H., Akiyama, S., Tomita, M., Soga, T., & Maruyama, S. (2021). Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry. Metabolites, 11(10), 671. https://doi.org/10.3390/metabo11100671

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