Location-Specific Oral Microbiome Possesses Features Associated With CKD

Abstract

Introduction: Chronic kidney disease (CKD), a progressive loss of renal function, can lead to serious complications if underdiagnosed. Many studies suggest that the oral microbiota plays important role in the health of the host; however, little is known about the association between the oral microbiota and CKD pathogenesis.

Methods: In this study, we surveyed the oral microbiota in saliva, the left and right molars, and the anterior mandibular lingual area from 77 participants (18 with and 59 without CKD), and tested their association with CKD to identify microbial features that may be predictive of CKD status.

Results: The overall oral microbiota composition significantly differed by oral locations and was associated with CKD status in saliva and anterior mandibular lingual samples. In CKD patients, we observed a significant enrichment of Neisseria and depletion of Veillonella in both sample types and a lower prevalence of Streptococcus in saliva after adjustment for other comorbidities. Furthermore, we detected a negative association of Neisseria and Streptococcus genera with the kidney function as measured by estimated glomerular filtration rate. Neisseria abundance also correlated with plasma interleukin-18 levels.

Conclusion: We demonstrate the association of the oral microbiome with CKD and inflammatory kidney biomarkers, highlighting a potential role of the commensal bacteria in CKD pathogenesis. A better understanding of the interplay between the oral microbiota and CKD may help in the development of new strategies to identify at-risk individuals or to serve as a novel target for therapeutic intervention.

Keywords: chronic kidney disease; dental plaque; oral microbiome; saliva.

Figure 1

Overall oral microbiome diversity by sampling location. (a) Comparison of the relative microbiota abundance (β diversity) between oral locations by the permutational multivariate analysis of variance (PERMANOVA) test. P values ≤ 0.001 with 999 permutations for all comparisons except for left and right molar (P = 0.74 with 999 permutations). The Bray−Curtis distance matrices were visualized using a nonmetric multiple dimensional scaling plot. (b) Comparison of the overall bacterial diversity (α diversity) between sampling locations (**P < 0.01, ***P < 0.001 by Student t test).

Figure 2

Oral microbial features associated with chronic kidney disease (CKD). (a) The cladoplots depict differential oral microbial features selected by linear discriminant analysis effect size analysis by CKD status in anterior mandibular lingual and saliva samples. Differential taxa between CKD and no CKD are demonstrated in color for the most abundant class: green indicating increase and red indicating reduction in CKD patients. (b) Comparison of the relative abundance of selected microbial features by CKD status. P value 1 (P1) was obtained from a Wilcoxon–Mann–Whitney test. P value 2 (P2) was obtained from a multivariate regression assuming a γ distribution for taxa and normal distribution for ratios while adjusting for type 2 diabetes, hypertension, coronary heart disease, periodontal disease, and body mass index. After multivariable adjustment, the association of CKD status with bacterial genera and ratios remained significant. (c) Receiver operating characteristic (ROC) curves and area under the curve (AUC) values to indicate the diagnostic accuracy of the selected features to predict CKD status.

Figure 3

Correlation analysis of the saliva microbiome with serum biomarkers. (a,b) Spearman correlation analyses conducted among the following: (a) the 5 most differential genera selected from the linear discriminant analysis effect size analysis, 6 plasma biomarkers, and estimated glomerular filtration rate (eGFR), and (b) 22 operational taxonomic units (OTUs) from the 5 genera, 6 plasma biomarkers, and eGFR. The results are presented as a heatmap and are grouped using unsupervised clustering. The scale ranges from +1.0 (red) to −1.0 (blue). An asterisk (*) indicates a Spearman rho > 0.3 or rho < −0.3. Circled in blue are correlations that survived the correction for eGFR using Spearman partial correlation analysis. (c,d) Correlation network constructed using the Fruchterman−Reingold layout in the R [Igraph] package. The nodes of the network represent the genera (c) or OTUs (d), plasma biomarkers and eGFR, where the edges (i.e., connections) correspond to a significant (P < 0.05, q < 0.05) and negative (blue, Spearman rho < −0.3) or positive (red, Spearman rho > 0.3) correlation between the nodes. The size of the nodes represents relative abundance of bacterial taxa.