Autor: |
Liam E Broughton-Neiswanger, Sol M Rivera-Velez, Martin A Suarez, Jennifer E Slovak, Pablo E Piñeyro, Julianne K Hwang, Nicolas F Villarino |
Jazyk: |
angličtina |
Rok vydání: |
2020 |
Předmět: |
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Zdroj: |
PLoS ONE, Vol 15, Iss 2, p e0228989 (2020) |
Druh dokumentu: |
article |
ISSN: |
1932-6203 |
DOI: |
10.1371/journal.pone.0228989 |
Popis: |
Prediction and early detection of kidney damage induced by nonsteroidal anti-inflammatories (NSAIDs) would provide the best chances of maximizing the anti-inflammatory effects while minimizing the risk of kidney damage. Unfortunately, biomarkers for detecting NSAID-induced kidney damage in cats remain to be discovered. To identify potential urinary biomarkers for monitoring NSAID-based treatments, we applied an untargeted metabolomics approach to urine collected from cats treated repeatedly with meloxicam or saline for up to 17 days. Applying multivariate analysis, this study identified a panel of seven metabolites that discriminate meloxicam treated from saline treated cats. Combining artificial intelligence machine learning algorithms and an independent testing urinary metabolome data set from cats with meloxicam-induced kidney damage, a panel of metabolites was identified and validated. The panel of metabolites including tryptophan, tyrosine, taurine, threonic acid, pseudouridine, xylitol and lyxitol, successfully distinguish meloxicam-treated and saline-treated cats with up to 75-100% sensitivity and specificity. This panel of urinary metabolites may prove a useful and non-invasive diagnostic tool for monitoring potential NSAID induced kidney injury in feline patients and may act as the framework for identifying urine biomarkers of NSAID induced injury in other species. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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