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. 2023 Aug 1;4(8):1048-1057.
doi: 10.34067/KID.0000000000000158. Epub 2023 Jun 9.

Urinary Metabolite Profile Predicting the Progression of CKD

Yaerim Kim  1 , Jueun Lee  2 , Mi Sun Kang  2 , Jeongin Song  3 , Seong Geun Kim  4 , Semin Cho  5 , Hyuk Huh  6 , Soojin Lee  7 , Sehoon Park  3   8 , Hyung Ah Jo  9 , Seung Hee Yang  10 , Jin Hyuk Paek  1 , Woo Yeong Park  1 , Seung Seok Han  3 , Hajeong Lee  3 , Jung Pyo Lee  11   12 , Kwon Wook Joo  3   12 , Chun Soo Lim  11   12 , Geum-Sook Hwang  2   13 , Dong Ki Kim  3   12
Affiliations

Urinary Metabolite Profile Predicting the Progression of CKD

Yaerim Kim et al. Kidney360. .

Abstract

Key Points:

  1. As a biomarker, urinary metabolites could bridge the gap between genetic abnormalities and phenotypes of diseases.

  2. We found that levels of betaine, choline, fumarate, citrate, and glucose were significantly correlated with kidney function and could predict kidney outcomes, providing prognostic biomarkers in CKD.

Background: Because CKD is caused by genetic and environmental factors, biomarker development through metabolomic analysis, which reflects gene-derived downstream effects and host adaptation to the environment, is warranted.

Methods: We measured the metabolites in urine samples collected from 789 patients at the time of kidney biopsy and from urine samples from 147 healthy participants using nuclear magnetic resonance. The composite outcome was defined as a 30% decline in eGFR, doubling of serum creatinine levels, or end-stage kidney disease.

Results: Among the 28 candidate metabolites, we identified seven metabolites showing (1) good discrimination between healthy controls and patients with stage 1 CKD and (2) a consistent change in pattern from controls to patients with advanced-stage CKD. Among the seven metabolites, betaine, choline, glucose, fumarate, and citrate showed significant associations with the composite outcome after adjustment for age, sex, eGFR, the urine protein–creatinine ratio, and diabetes. Furthermore, adding choline, glucose, or fumarate to traditional biomarkers, including eGFR and proteinuria, significantly improved the ability of the net reclassification improvement (P < 0.05) and integrated discrimination improvement (P < 0.05) to predict the composite outcome.

Conclusion: Urinary metabolites, including betaine, choline, fumarate, citrate, and glucose, were found to be significant predictors of the progression of CKD. As a signature of kidney injury–related metabolites, it would be warranted to monitor to predict the renal outcome.

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Conflict of interest statement

S.S. Han reports the following—research funding: Daewoong Pharmaceutical. C.S. Lim reports the following—advisory or leadership role: President, Korean Society of Nephrology. All remaining authors have nothing to disclose.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
The quantitative concentrations of eight selected metabolites. Box plots showing the creatinine-adjusted quantitative urinary metabolite concentration. The y axis indicates the creatinine-adjusted urine concentration (μM/mM Cr). The box plot shows the interquartile ranges, with the horizontal lines indicating the median values. Each dot represents an outlier over the 95th percentile range. CTL, control.
Figure 2.
Figure 2.
Kaplan‒Meier curves for the composite outcome according to each metabolite. The risk for the composite outcome is presented according to the tertiles of each metabolite. Blue, green, and yellow lines indicate the first, second, and third tertiles, respectively. The y axis indicates the risk proportion, and the x axis shows the follow-up period in months. TMAO, trimethylamine-N-oxide.
See this image and copyright information in PMC

References

    1. Nicholson JK, Lindon JC. Systems biology: metabonomics. Nature. 2008;455(7216):1054–1056. doi:10.1038/4551054a - DOI - PubMed
    1. Guma M, Tiziani S, Firestein GS. Metabolomics in rheumatic diseases: desperately seeking biomarkers. Nat Rev Rheumatol. 2016;12(5):269–281. doi:10.1038/nrrheum.2016.1 - DOI - PMC - PubMed
    1. Posada-Ayala M Zubiri I Martin-Lorenzo M, et al. . Identification of a urine metabolomic signature in patients with advanced-stage chronic kidney disease. Kidney Int. 2014;85(1):103–111. doi:10.1038/ki.2013.328 - DOI - PubMed
    1. Gao X Chen W Li R, et al. . Systematic variations associated with renal disease uncovered by parallel metabolomics of urine and serum. BMC Syst Biol. 2012;6(S1):S14. doi:10.1186/1752-0509-6-s1-s14 - DOI - PMC - PubMed
    1. Missailidis C Hallqvist J Qureshi AR, et al. . Serum trimethylamine-N-oxide is strongly related to renal function and predicts outcome in chronic kidney disease. PloS One. 2016;11(1):e0141738. doi:10.1371/journal.pone.0141738 - DOI - PMC - PubMed

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