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
. 2016 May;27(5):1389-99.
doi: 10.1681/ASN.2015030279. Epub 2015 Sep 23.

The Influence of CKD on Colonic Microbial Metabolism

Ruben Poesen  1 , Karen Windey  2 , Ellen Neven  3 , Dirk Kuypers  1 , Vicky De Preter  3 , Patrick Augustijns  4 , Patrick D'Haese  3 , Pieter Evenepoel  1 , Kristin Verbeke  2 , Björn Meijers  5
Affiliations

The Influence of CKD on Colonic Microbial Metabolism

Ruben Poesen et al. J Am Soc Nephrol. 2016 May.

Abstract

There is increasing interest in the colonic microbiota as a relevant source of uremic retention solutes accumulating in CKD. Renal disease can also profoundly affect the colonic microenvironment and has been associated with a distinct colonic microbial composition. However, the influence of CKD on the colonic microbial metabolism is largely unknown. Therefore, we studied fecal metabolite profiles of hemodialysis patients and healthy controls using a gas chromatography-mass spectrometry method. We observed a clear discrimination between both groups, with 81 fecal volatile organic compounds detected at significantly different levels in hemodialysis patients and healthy controls. To further explore the differential impact of renal function loss per se versus the effect of dietary and other CKD-related factors, we also compared fecal metabolite profiles between patients on hemodialysis and household contacts on the same diet, which revealed a close resemblance. In contrast, significant differences were noted between the fecal samples of rats 6 weeks after 5/6th nephrectomy and those of sham-operated rats, still suggesting an independent influence of renal function loss. Thus, CKD associates with a distinct colonic microbial metabolism, although the effect of renal function loss per se in humans may be inferior to the effects of dietary and other CKD-related factors. The potential beneficial effect of therapeutics targeting colonic microbiota in patients with CKD remains to be examined.

Keywords: chronic kidney disease; intestine; nutrition.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Fecal metabolite profiles of patients on hemodialysis and healthy controls. (A) Hierarchical cluster analysis. Rows represent fecal metabolite profiles of patients on hemodialysis (CKD, solid blue) and healthy controls (control, green). Red indicates increased abundance of individual VOCs relative to internal standard and blue indicates decreased abundance. (B) PCA score plot of fecal metabolite profiles of patients on hemodialysis (solid blue square) and healthy controls (green circle).
Figure 2.
Figure 2.
PLS-DA of fecal metabolite profiles of hemodialysis patients and healthy controls. (A) PLS-DA score plot of fecal metabolite profiles of patients on hemodialysis (solid blue square) and healthy controls (green circle). (B) Leave-one-out cross-validation and prediction analysis from the PLS-DA model for patients on hemodialysis (solid blue square), healthy controls (green circle) and five additional unrelated patients on hemodialysis (blue box); the predicted group of patients on hemodialysis has a target value of y=1, the group of healthy controls has a target value of y=0, and the discriminant threshold (y=0.5) is the dashed line.
Figure 3.
Figure 3.
PLS-DA of fecal metabolite profiles of patients on hemodialysis and age-matched healthy controls. (A) PLS-DA score plot of fecal metabolite profiles of patients on hemodialysis (solid blue square) and age-matched healthy controls (black diamond). (B) Leave-one-out cross-validation and prediction analysis from the PLS-DA model for patients on hemodialysis (solid blue square), age-matched healthy controls (black diamond), and five additional unrelated patients on hemodialysis (blue box). (C) PLS-DA analysis score plot of fecal metabolite profiles of nondiabetic patients on hemodialysis (solid blue square) and age-matched healthy controls (black diamond). (D) Leave-one-out cross-validation and prediction analysis from the PLS-DA model for nondiabetic patients on hemodialysis (solid blue square), age-matched healthy controls (black diamond), and five additional unrelated patients on hemodialysis (blue box); the predicted group of hemodialysis patients has a target value of y=1, the group of healthy controls has a target value of y=0, and the discriminant threshold (y=0.5) is the dashed line.
Figure 4.
Figure 4.
Fecal metabolite profiles of patients on hemodialysis and household contacts on the same diet. (A) PLS-DA score plot of fecal metabolite profiles of patients on hemodialysis (solid blue square) and household contacts on the same diet (red triangle). (B) Leave-one-out cross-validation and prediction analysis from the PLS-DA model for patients on hemodialysis (solid blue square), household contacts on the same diet (red triangle), and five additional unrelated patients on hemodialysis (blue box); the predicted group of patients on hemodialysis has a target value of y=0, the group of household contacts on the same diet has a target value of y=1, and the discriminant threshold (y=0.5) is the dashed line.
Figure 5.
Figure 5.
Fecal metabolite profiles of patients on hemodialysis, healthy controls, age-matched healthy controls, and household contacts on the same diet. PLS-DA score plot of fecal metabolite profiles of patients on hemodialysis (solid blue square), healthy controls (green circle), age-matched healthy controls (black diamond), and household contacts on the same diet (red triangle).
Figure 6.
Figure 6.
Fecal metabolite profiles of rats 6 weeks after induction of 5/6th nephrectomy and after sham operation. (A) PLS-DA score plot of fecal metabolite profiles of rats 6 weeks after induction of 5/6th nephrectomy (CKD rats, solid blue square) and after sham operation (control rats, green circle). (B) Leave-one-out cross-validation from the PLS-DA model for rats with CKD (solid blue square) and control rats (green circle); the predicted group of rats with CKD has a target value of y=1, the group of control rats has a target value of y=0, and the discriminant threshold (y=0.5) is the dashed line.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Shanahan F: The gut microbiota in 2011: Translating the microbiota to medicine. Nat Rev Gastroenterol Hepatol 9: 72–74, 2012 - PubMed
    1. Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC, Siuzdak G: Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A 106: 3698–3703, 2009 - PMC - PubMed
    1. Aronov PA, Luo FJ, Plummer NS, Quan Z, Holmes S, Hostetter TH, Meyer TW: Colonic contribution to uremic solutes. J Am Soc Nephrol 22: 1769–1776, 2011 - PMC - PubMed
    1. Meyer TW, Hostetter TH: Uremic solutes from colon microbes. Kidney Int 81: 949–954, 2012 - PubMed
    1. Evenepoel P, Meijers BK, Bammens BR, Verbeke K: Uremic toxins originating from colonic microbial metabolism. Kidney Int Suppl 114: S12–S19, 2009 - PubMed

Publication types