Peer-reviewed veterinary case report
Early kidney disease in cats detected by 3-hydroxykynurenine and AI
By Ellen Vanden Broecke et al.·Published in Scientific Reports·2025·Faculty of Veterinary Medicine, Department of Translational Physiology, Infectiology and Public Health, Laboratory of Integrative Metabolomics (LIMET), Ghent University, GB·View original on DOAJ →
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Original publication title: Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning
- Species:
- cat
Plain-English summary
A study found that a specific substance in the blood and urine called 3-hydroxykynurenine could help detect early signs of chronic kidney disease (CKD) in cats, especially those over five years old. This condition is common and often goes unnoticed until it's advanced, making early detection crucial for better management. Researchers compared healthy cats to those with early-stage CKD and discovered that measuring this biomarker could identify the disease up to six months earlier than current tests. This advancement could lead to better treatment options and improved outcomes for affected cats.
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Abstract
Abstract Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests fail to diagnose early CKD. Therefore, this study aimed to identify early metabolic biomarkers. First, cats were retrospectively divided into two populations to conduct a case–control study, comparing the urinary and serum metabolome of healthy (n = 61) and CKD IRIS stage 2 cats (CKD2, n = 63). Subsequently, longitudinal validation was conducted in an independent population comprising healthy cats that remained healthy (n = 26) and cats that developed CKD2 (n = 22) within one year. Univariate, multivariate, and machine learning-based (ML) approaches were compared. The serum-to-urine ratio of 3-hydroxykynurenine was identified as a single biomarker candidate, yielding a high AUC (0.844) and accuracy (0.804), while linear support vector machine-based modelling employing metabolites and clinical parameters enhanced AUC (0.929) and accuracy (0.862) six months before traditional diagnosis. Furthermore, analysis of variable importance indicated consistent key serum metabolites, namely creatinine, SDMA, 2-hydroxyethanesulfonate, and aconitic acid. By enabling accurate diagnosis at least six months earlier, the highlighted metabolites may pave the way for improved diagnostics, ultimately contributing to timely disease management.
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Search related cases →Original publication on DOAJ: https://doi.org/10.1038/s41598-025-90019-x