{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Saito R"],"funding":["National Institute of Diabetes and Digestive and Kidney Diseases","Japan Agency for Medical Research and Development","Japan Science and Technology Agency","Japan Society for the Promotion of Science"],"pagination":["671"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8540909"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(10)"],"pubmed_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."],"journal":["Metabolites"],"pubmed_title":["Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry."],"pmcid":["PMC8540909"],"funding_grant_id":["OPERA JPMJOP1842","JP21ek0109544","1R01DK110541-01","KAKENHI JP20H05743","KAKENHI JP19K08689","KAKENHI JP18K08219"],"pubmed_authors":["Saito R","Kato Y","Soga T","Tomita M","Ikeda S","Hirayama A","Kwan B","Maruyama S","Shinjo H","Natarajan L","Pu M","Kamei Y","Akiba A","Akiyama S"],"additional_accession":[]},"is_claimable":false,"name":"Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry.","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.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Sep","modification":"2025-04-18T17:27:46.519Z","creation":"2025-04-07T04:58:37.151Z"},"accession":"S-EPMC8540909","cross_references":{"pubmed":["34677386"],"doi":["10.3390/metabo11100671"]}}