Alteration of the Gut Microbiome in Chronic Kidney Disease Patients and Its Association With Serum Free Immunoglobulin Light Chains

Abstract

Objectives: Gut dysbiosis is associated with chronic kidney disease (CKD), and serum free immunoglobulin light chains (FLCs) are biomarkers for CKD. This study aims to assess the CKD gut microbiome and to determine its impact on serum FLC levels.

Methods: To control for confounders, 100 patients and sex- and age-matched healthy controls (HCs) were recruited. The gut microbiome was assessed by sequencing 16S rRNA gene V3-V4 hypervariable regions. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States was applied to infer functional metabolic pathways. When observing group differences in the microbiome and predicted metabolic pathways, demographic confounders were adjusted using binary logistic regression; when examining impacts of the gut microbiome and metabolic pathways on serum FLCs, factors influencing FLC levels were adjusted using multiple regression.

Results: Principal coordinate analysis revealed a significantly different bacterial community between the CKD and HC groups (P < 0.05). After adjusting for confounders, lower Chao 1, observed species and Shannon indices based on binary logistic regression predicted CKD prevalence. Actinobacteria, Alistipes, Bifidobacterium and Bifidobacterium longum enrichment, upregulation of metabolic pathways of bacterial toxin, chloroalkane and chloroalkene degradation, and Staphylococcus aureus infection also predicted CKD prevalence (P < 0.05). Furthermore, depletion of Actinobacteria and Bifidobacterium and reduced chloroalkane and chloroalkene degradation predicted high levels of FLC λ (P < 0.05).

Conclusions: Gut dysbiosis in CKD patients was confirmed by controlling for confounders in the present study. Additionally, the association between gut dysbiosis and FLC λ levels demonstrates the existence of crosstalk between the microbiome and immune response in CKD.

Keywords: Bifidobacterium; chronic kidney disease; confounders; free immunoglobulin light chains; gut microbiome.

Figure 1

Bacterial community, diversity and profile. (A) Principal coordinates analysis (PCoA) revealed clustering of bacterial taxa in the CKD and HC groups based on Bray–Curtis distance, with each point corresponding to a subject and colored according to the sample type. Permutational multivariate analysis of variance showed that the separation of bacterial communities in the CKD and HC cohort was significant (P = 0.001). (B) Venn diagram showing the shared number of operational taxonomic units by CKD and HC subjects. (C) Bacterial richness and diversity between the CKD and HC cohorts. Binary regression analysis was used to adjust for confounders of BMI, hypertension and hyperlipidemia. The estimated ORs and their CIs are displayed as forest graphs. The blue dotted line represents the OR value = 1. Comparison of gut microbiome richness and diversity shows that lower Chao 1, observed species and Shannon indices can predict the prevalence of CKD (P < 0.05). (D) Bacterial profile in the CKD and HC groups. Red font represents the CKD group and the bacterial abundance in this group; black font represents the HC group and the bacterial abundance in this group.

Figure 2

Bacterial abundance showing a significant difference between the CKD and HC groups when adjusting for confounders (A–C). Binary regression analysis was used to adjust for confounders of BMI, hypertension and hyperlipidemia. The estimated ORs and their CIs are displayed as forest graphs. The blue dotted line represents the OR value = 1.

Figure 3

Comparison of functional pathways between the CKD and HC groups (A–E). Gene functions were predicted based on 16S rRNA gene-based microbial compositions using the PICRUSt algorithm and the Kyoto Encyclopedia of Genes and Genomes database. Binary regression analysis was used to adjust for confounders of BMI, hypertension and hyperlipidemia. The estimated ORs and their CIs are displayed as forest graphs. The blue dotted line represents the OR value = 1.