期刊
PEDIATRIC NEPHROLOGY
卷 37, 期 9, 页码 2079-2090出版社
SPRINGER
DOI: 10.1007/s00467-021-05380-6
关键词
Biomarker; Pediatric; Acute kidney injury; Metabolomics
资金
- BC Children's Hospital Foundation
- McGill University Health Centre Research Institute
- Kidney Research Scientist Core Education
- National Training Program
- Fonds de Recherches en Sante du Quebec
- Fonds de Recherches du Quebec-Sante
- National Institutes of Health (NIH) [P50DK096418]
This study demonstrates the potential of a urine metabolite classifier to detect the risk of acute kidney injury in pediatric populations earlier than the current standard of diagnosis.
Background Acute kidney injury (AKI) is characterized by an abrupt decline in glomerular filtration rate (GFR). We sought to identify separate early urinary metabolomic signatures at AKI onset (with-AKI) and prior to onset of functional impairment (pre-AKI). Methods Pre-AKI (n=15), AKI (n=22), and respective controls (n=30) from two prospective PICU cohort studies provided urine samples which were analyzed by GC-MS and DI-MS mass spectrometry (193 metabolites). The cohort (n=58) was 8.7 +/- 6.4 years old and 66% male. AKI patients had longer PICU stays, higher PRISM scores, vasopressors requirement, and respiratory diagnosis and less commonly had trauma or post-operative diagnosis. Urine was collected within 2-3 days after admission and daily until day 5 or 14. Results The metabolite classifiers for pre-AKI samples (1.5 +/- 1.1 days prior to AKI onset) had a cross-validated area under receiver operator curve (AUC)=0.93 (95%CI 0.85-1.0); with-AKI samples had an AUC=0.94 (95%CI 0.87-1.0). A parsimonious pre-AKI classifier with 13 metabolites was similarly robust (AUC=0.96, 95%CI 0.89-1.0). Both classifiers were similar and showed modest correlation of high-ranking metabolites (tau=0.47, p<0.001). Conclusions This exploratory study demonstrates the potential of a urine metabolite classifier to detect AKI-risk in pediatric populations earlier than the current standard of diagnosis with the need for external validation.
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