ArticleMetabolites2021
Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry.
Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Untargeted urinary metabolomics reveals metabolic alterations and severity biomarkers in cardiac surgery-associated acute kidney injury.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Can Novel Biomarkers Effectively Predict Acute Kidney Injury in Liver or Kidney Transplant Recipients?International journal of molecular sciences · 2024Review
- CKD Urine Metabolomics: Modern Concepts and Approaches.Pathophysiology : the official journal of the International Society for Pathophysiology · 2023Review
- The association between albumin corrected anion gap and ICU mortality in acute kidney injury patients requiring continuous renal replacement therapy.Internal and emergency medicine · 2022Article
- Cellular and Functional Biomarkers of Renal Injury and Disease.Current opinion in toxicology · 2022Article
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.