Secondary Sclerosing Cholangitis in Critically Ill Patients Alters the Gut-Liver Axis: A Case Control Study

Abstract

Secondary sclerosing cholangitis in critically ill patients (SC-CIP) occurs after long-term intensive care treatment. This study aimed to assess the gut-liver axis in SC-CIP. Stool microbiome composition, gut permeability, bacterial translocation and serum bile acid profiles of 18 SC-CIP patients compared to 11 patients after critical illness without liver disease (CIP controls), 21 patients with cirrhosis and 21 healthy controls were studied. 16S rDNA was isolated from stool and sequenced using the Illumina technique. Diamine oxidase, zonulin, soluble CD14 (sCD14) and lipopolysaccharide binding protein were measured in serum and calprotectin in stool. Serum bile acids were analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS). Reduced microbiome alpha diversity and altered beta diversity were seen in SC-CIP, CIP controls and cirrhosis compared to healthy controls. SC-CIP patients showed a shift towards pathogenic taxa and an oralization. SC-CIP, CIP controls and cirrhotic patients presented with impaired gut permeability, and biomarkers of bacterial translocation were increased in SC-CIP and cirrhosis. Total serum bile acids were elevated in SC-CIP and cirrhosis and the bile acid profile was altered in SC-CIP, CIP controls and cirrhosis. In conclusions, observed alterations of the gut-liver axis in SC-CIP cannot solely be attributed to liver disease, but may also be secondary to long-term intensive care treatment.

Keywords: SC-CIP; bile acids; critical illness; gut permeability; microbiome; secondary sclerosing cholangitis.

Figure 1

Boxplots showing levels of mean alpha diversity of Secondary Sclerosing Cholangitis in Critically Ill Patients (SC-CIP) (red), patients after critical illness without liver disease (CIP controls) (violet), cirrhosis (green) and healthy (yellow) groups using Chao 1 (A) and Richness Index (B). Significances are indicated by stars. *** p ≤ 0.001. For statistical analysis, analysis of variance (ANOVA) with Bonferroni correction was used.

Figure 2

Redundancy analysis (RDA analysis) for beta diversity based on BrayCurtis dissimilarity on the genus level. Each point on the diagram presents one study proband. Each group is drawn in a unique color. (A) SC-CIP (yellow) compared to healthy (green) (p = 0.001), and (B) SC-CIP (red), CIP controls (green), cirrhosis (yellow) and healthy (violet) compared to each other (p = 0.001). Findings indicated that with RDA analysis, all four groups showed differences in beta diversity. PC = principal component.

Figure 3

RDA plots for statin intake and cirrhosis. Each point on the diagram presents one study proband. (A) People not taking statins are shown in green, people taking statins are shown in orange (p = 0.004). (B) Patients with cirrhosis are drawn green, controls not suffering from cirrhosis in yellow (p = 0.004). In both graphs, clustering of belonging groups can be observed, indicating that cirrhosis and statin intake are possible explanatory variables for microbiome composition.

Figure 4

Principal coordinates analysis (PCoA) Plot for Network Analysis for beta diversity using Spearman’s rho correlation. Taxa are drawn as dots, taxa abundance as dot size and edges show positive and negative associations. Green: statin intake, red: SC-CIP, purple: CIP controls, blue: cirrhosis, yellow: healthy. With overlap of colors, between groups similarities of microbiome composition are indicated. The described overlaps of the genera Blautia and Lactobacillus are marked with purple circles, the close relation of the group with statin intake and healthy is marked with a yellow circle.

Figure 5

Linear discriminant analysis-effect size (LEfse) results of SC-CIP (red), CIP controls (violet), cirrhosis (green) and healthy (Yellow) groups. The presented genera are associated with microbiome composition of the respective group. The length of the bar shows the logarithmic linear discriminant analysis (LDA) score.

Figure 6

Boxplots for markers of gut permeability, bacterial translocation and intestinal and systemic inflammation. (A) diaminoxidase (B) zonulin (C) calprotectin (D) c-reactive protein (E) lipopolysaccharide-binding protein (F) soluble cluster of differentiation 14. SC-CIP (red), CIP controls (violet), cirrhosis (green), healthy (yellow). Significances are indicated with bars and stars within the diagrams. * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001. For statistical analysis, analysis of variance (ANOVA) with Bonferroni correction was used; DAO = diaminoxidase; CRP = c-reactive protein; LBP = lipopolysaccharide-binding protein; sCD14 = soluble cluster of differentiation 14.

Figure 7

Boxplots for comparison of bile acid levels. (A) total bile acids (B) primary bile acids (C) secondary bile acids (D) ratio primary to secondary bile acids (E) total conjugated bile acids (F) total unconjugated bile acids (G) ratio conjugated to unconjugated bile acids (H) ursodexocholic acid (I) chenodeoxycholic acid (J) cholic acid (K) desoxycholic acid (L) lithocholic acid.SC-CIP (red), CIP controls (violet), cirrhosis (green), healthy (yellow). Significances are indicated with bars and stars within the diagrams. * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001. For statistical analysis, analysis of variance (ANOVA) with Bonferroni correction was used.

Figure 8

Principal coordinates analysis (PCoA) plot for serum bile acid composition using non-metric multidimensional scaling (Bray–Curtis) for analysis. Orange: SC-CIP, violet: CIP controls, turquoise: cirrhosis, yellow: healthy. Each point on the diagram presents one study proband. Significantly different bile acid profiles were found in healthy compared to SC-CIP, CIP control and cirrhosis groups and between SC-CIP and cirrhosis groups. R = 0.26, p < 0.001; SC-CIP–healthy: p < 0.001; cirrhosis–healthy: p < 0.001; CIP controls–healthy: p = 0.006. NMDS = Non-metric multidimensional scaling.