TY - JOUR
T1 - Urinary metabolome analyses of patients with acute kidney injury using capillary electrophoresis-mass spectrometry
AU - Saito, Rintaro
AU - Hirayama, Akiyoshi
AU - Akiba, Arisa
AU - Kamei, Yushi
AU - Kato, Yuyu
AU - Ikeda, Satsuki
AU - Kwan, Brian
AU - Pu, Minya
AU - Natarajan, Loki
AU - Shinjo, Hibiki
AU - Akiyama, Shin’Ichi
AU - Tomita, Masaru
AU - Soga, Tomoyoshi
AU - Maruyama, Shoichi
N1 - Funding Information:
Funding: Research activities in the Institute for Advanced Biosciences, Keio University were funded by grants from Yamagata Prefecture and Tsuruoka City. R.S. was funded by JSPS KAKENHI (grant numbers JP19K08689 and JP20H05743) and JST OPERA (grant number JPMJOP1842). A.H. was funded by JSPS KAKENHI (grant number JP18K08219). A.H., S.A. and S.M. were funded by grants from AMED (grant number JP21ek0109544). B.K., M.P. and L.N. were funded by grants from NIDDK (grant number 1R01DK110541-01).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/10
Y1 - 2021/10
N2 - 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: 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.
AB - 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: 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.
KW - AKI
KW - Biomarker
KW - Capillary electrophoresis-mass spectrometry (CE-MS)
KW - Urine
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U2 - 10.3390/metabo11100671
DO - 10.3390/metabo11100671
M3 - Article
AN - SCOPUS:85116171183
SN - 2218-1989
VL - 11
JO - Metabolites
JF - Metabolites
IS - 10
M1 - 671
ER -