Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Dec;45(1):1-15.
doi: 10.1080/01652176.2024.2447601. Epub 2025 Jan 2.

Metabolomics reveals alterations in gut-derived uremic toxins and tryptophan metabolism in feline chronic kidney disease

Affiliations

Metabolomics reveals alterations in gut-derived uremic toxins and tryptophan metabolism in feline chronic kidney disease

Laurens Van Mulders et al. Vet Q. 2025 Dec.

Abstract

Chronic Kidney Disease (CKD) is one of the most common conditions affecting felines, yet the metabolic alterations underlying its pathophysiology remain poorly understood, hindering progress in identifying biomarkers and therapeutic targets. This study aimed to provide a comprehensive view of metabolic changes in feline CKD across conserved biochemical pathways and evaluate their progression throughout the disease continuum. Using a multi-biomatrix high-throughput metabolomics approach, serum and urine samples from CKD-affected cats (n = 94) and healthy controls (n = 84) were analyzed with ultra-high-performance liquid chromatography-high-resolution mass spectrometry. Significant disruptions were detected in tryptophan (indole, kynurenine, serotonin), tyrosine, and carnitine metabolism, as well as in the urea cycle. Circulating gut-derived uremic toxins, including indoxyl-sulfate, p-cresyl-sulfate, and trimethylamine-N-oxide, were markedly increased, primarily due to impaired renal excretion. However, alternative mechanisms, such as enhanced bacterial formation from dietary precursors like tryptophan, tyrosine, carnitine, and betaine, could not be ruled out. Overall, the findings suggest that metabolic disturbances in feline CKD are largely driven by the accumulation of gut-derived uremic toxins derived from precursors highly abundant in the feline diet. These insights may link the strict carnivorous nature of felines to CKD pathophysiology and highlight potential avenues for studying preventive or therapeutic interventions.

Keywords: Feline chronic kidney disease; biochemical pathway analysis; fundamental nephrology; gut microbiota derived uremic toxins; metabolomics; pathophysiology.

PubMed Disclaimer

Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

None
Graphical abstract
Figure 1.
Figure 1.
The study design consists of a comparison between the serum and urine metabolome of a group of cats diagnosed with chronic kidney disease (CKD; n = 94) and a healthy control (n = 84) group. Serum and urine samples were collected, followed by chemical extraction according to feline-specific optimized and validated methods. Samples from both matrices were analyzed by Ultra-High-performance liquid chromatography coupled to high-Resolution Mass spectrometry (UHPLC-HRMS). Generated data was further analyzed using an untargeted approach to discover which pathways are altered in feline CKD. Specific metabolites in these pathways were identified in a targeted manner and statistically compared between the two health states, based on this the biological relevance of the disrupted metabolic pathways was further elucidated.
Figure 2.
Figure 2.
A: PCA-X score plot visualizing natural variance in the serum metabolome among healthy controls and different CKD stages: Healthy (green, n = 84), stage 2 CKD (pink, n = 59), stage 3 CKD (red, n = 29), and stage 4 CKD (dark red, n = 6). Figure 2B: Score plot of the supervised OPLS-DA model showing distinct separation between CKD (dark red, n = 94) and healthy (green, n = 84) states according to the serum metabolome. The model exhibits excellent performance characteristics (R2Y:0.96, Q2:0.71). Figure 2C: Visual representation of the most dysregulated metabolic pathways in feline CKD, based on metabolic fingerprinting, using the mummichog algorithm. The X-axis represents the enrichment factor and is derived from the number of hits within a particular metabolic pathway (increasing range: small to large dots). The Y-axis represents the significance level (-log10(p)) of the perturbed pathway.
Figure 3.
Figure 3.
Heat map of all a priori selected serum metabolites, representing natural clustering between healthy cats (n = 84) and cats diagnosed with CKD (n = 94) at the group level and inherent clustering of up- or downregulated or unaltered metabolites at the compound level.
Figure 4.
Figure 4.
Box plot representation of the selected serum and urine metabolites from the tryptophan metabolism comparing healthy controls (green; n = 84) with specific CKD stages. Significance levels (*<0.05; **<0.001) for the comparison healthy vs. CKD (all stages) are provided. Clear up- or downward trends in the indole, serotonin and kynurenine pathways are portrayed in accordance with CKD progression from stage 2 (pink; n = 59) to stage 3 (red; n = 29) and 4 (dark red; n = 6). Urine metabolites are normalized to urine creatinine.
Figure 5.
Figure 5.
A) Box plot representation of the selected serum and urine metabolites from the carnitine metabolism comparing healthy controls (green; n = 84) with specific disease stages. Significance levels (*<0.05; **<0.001) for the comparison healthy vs. CKD (all stages) are provided. Clear up- or downward trends are portrayed in accordance with CKD progression from stage 2 (pink; n = 59) to stage 3 (red; n = 29) and 4 (dark red; n = 6). Urine metabolites are normalized to urine creatinine. B) Trend lines representing the linear relationship between serum creatinine (X-axis) and specific serum metabolites (Y-axis) from the carnitine pathway. Red lines mean upward trends, blue lines mean downward trends and grey lines mean unaltered behavior in accordance with CKD progression from stage 2 to stage 3 and 4. Spearman correlation coefficients are provided with significance levels (*<0.05; **<0.001) for every metabolite. The positive ratio of betaine/carnitine and its catabolites is completely reversed precisely at the onset of stage 2 CKD.
Figure 6.
Figure 6.
Nutritional Precursors tryptophan, tyrosine, carnitine, and betaine are highly abundant in the diet of strict carnivores, such as felines. These Precursors are partially degraded in the gut to the bacterial byproducts indole, p-cresol, and trimethylamine (TMA). These compounds are subsequently processed by the liver, resulting in the formation of the uremic toxins indoxyl-sulfate, p-cresyl-sulfate, and trimethylamine-N-oxide (TMAO). These solutes undergo renal excretion by the tubule cells, which is associated with oxidative stress promoting tubular inflammation and secondary fibrosis. This process is hypothesized to contribute to the progression and development of feline CKD.

Similar articles

Cited by

References

    1. Acierno MJ, Brown S, Coleman AE, Jepson RE, Papich M, Stepien RL, Syme HM.. 2018. ACVIM consensus statement: guidelines for the identification, evaluation, and management of systemic hypertension in dogs and cats. J Vet Intern Med. 32(6):1803–1822. doi: 10.1111/jvim.15331. - DOI - PMC - PubMed
    1. Bahn A, Ljubojevic M, Lorenz H, Schultz C, Ghebremedhin E, Ugele B, Sabolic I, Burckhardt G, Hagos Y.. 2005. Murine renal organic anion transporters mOAT1 and mOAT3 facilitate the transport of neuroactive tryptophan metabolites. Am J Physiol Cell Physiol. 289(5):C1075–1084. doi: 10.1152/ajpcell.00619.2004. - DOI - PubMed
    1. Bain MA, Faull R, Fornasini G, Milne RW, Evans AM.. 2006. Accumulation of trimethylamine and trimethylamine-N-oxide in end-stage renal disease patients undergoing haemodialysis. Nephrol Dial Transplant. 21(5):1300–1304. doi: 10.1093/ndt/gfk056. - DOI - PubMed
    1. Bender DA. 1983. Biochemistry of tryptophan in health and disease. Mol Aspects Med. 6(2):101–197. doi: 10.1016/0098-2997(83)90005-5. - DOI - PubMed
    1. Bidin MZ, Shah AM, Stanslas J, Seong CLT.. 2019. Blood and urine biomarkers in chronic kidney disease: an update. Clin Chim Acta. 495(2019):239–250. doi: 10.1016/j.cca.2019.04.069. - DOI - PubMed

MeSH terms