Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry.
Rintaro SaitoAkiyoshi HirayamaArisa AkibaYushi KameiYuyu KatoSatsuki IkedaBrian KwanMinya PuLoki NatarajanHibiki ShinjoShin'ichi AkiyamaMasaru TomitaTomoyoshi SogaShoichi MaruyamaPublished in: Metabolites (2021)
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.
Keyphrases
- acute kidney injury
- cardiac surgery
- capillary electrophoresis
- mass spectrometry
- end stage renal disease
- machine learning
- minimally invasive
- ejection fraction
- newly diagnosed
- chronic kidney disease
- intensive care unit
- prognostic factors
- liquid chromatography
- ms ms
- artificial intelligence
- wastewater treatment
- drug induced