Serum Levels of Metabolites Produced by Intestinal Microbes and Lipid Moieties Independently Associated With Acute-on-Chronic Liver Failure and Death in Patients With Cirrhosis

Abstract

Background & aims: Inpatients with cirrhosis have high rates of acute-on-chronic failure (ACLF) development and high mortality within 30 days of admission to the hospital. Better biomarkers are needed to predict these outcomes. We performed metabolomic analyses of serum samples from patients with cirrhosis at multiple centers to determine whether metabolite profiles might identify patients at high risk for ACLF and death.

Methods: We performed metabolomic analyses, using liquid chromatography, of serum samples collected at time of admission to 12 North American tertiary hepatology centers from 602 patients in the North American Consortium for the Study of End-Stage Liver Disease sites from 2015 through 2017 (mean age, 56 years; 61% men; mean model for end-stage liver disease score, 19.5). We performed analysis of covariance, adjusted for model for end-stage liver disease at time of hospital admission, serum levels of albumin and sodium, and white blood cell count, to identify metabolites that differed between patients who did vs did not develop ACLF and patients who did vs did not die during hospitalization and within 30 days. We performed random forest analysis to identify specific metabolite(s) that were associated with outcomes and area under the curve (AUC) analyses to analyze them in context of clinical parameters. We analyzed microbiomes of stool samples collected from 133 patients collected at the same time and examined associations with serum metabolites.

Results: Of the 602 patients analyzed, 88 developed ACLF (15%), 43 died in the hospital (7%), and 72 died within 30 days (12%). Increased levels of compounds of microbial origin (aromatic compounds, secondary or sulfated bile acids, and benzoate) and estrogen metabolites, as well as decreased levels of phospholipids, were associated with development of ACLF, inpatient, and 30-day mortality and were also associated with fecal microbiomes. Random forest analysis and logistic regression showed that levels of specific microbially produced metabolites identified patients who developed ACLF with an AUC of 0.84 (95% confidence interval [CI] 0.78-0.88; P = .001), patients who died while in the hospital with an AUC of 0.81 (95% CI 0.74-0.85; P = .002), and patients who died within 30 days with an AUC of 0.77 (95% CI 0.73-0.81; P = .02). The metabolites were significantly additive to clinical parameters for predicting these outcomes. Metabolites associated with outcomes were also correlated with microbiomes of stool samples.

Conclusions: In an analysis of serum metabolites and fecal microbiomes of patients hospitalized with cirrhosis at multiple centers, we associated metabolites of microbial origin and lipid moieties with development of ACLF and death as an inpatient or within 30 days, after controlling for clinical features.

Keywords: Estrone; Sepsis; Tryptophan.

Figure 1:. ACLF-Related Serum Metabolites

Least square means of ANCOVA-corrected microbial-origin metabolites presented as mean and 95% CI between patients who did (marked as 1) and did not develop ACLF (marked as 0). A: Bile acids and Choline moieties, B: Aromatic compounds and C: Xenobiotics. Y axis: scaled intensity. FDR-corrected p values are shown for each metabolite.

Figure 2:. Inpatient Mortality-Related Serum Metabolites

Least square means of ANCOVA-corrected microbial-origin metabolites presented as mean and 95% CI between patients who did (marked as 1) and did not develop inpatient mortality (marked as 0). Y axis: scaled intensity. A: Bile acids moieties, B: Aromatic compounds and C: Xenobiotics FDR-corrected p values are shown for each metabolite.

Figure 3:. 30-day Mortality-Related Serum Metabolites

Least square means of ANCOVA-corrected microbial-origin metabolites presented as mean and 95% CI between patients who did (marked as 1) and did not develop 30-day mortality (marked as 0). Y axis: scaled intensity. A: Bile acids moieties, B: Aromatic compounds and C: Xenobiotics. FDR-corrected p values are shown for each metabolite.

Figure 4:. Mean Decrease Accuracy on Random Forest Analysis

Mean Decrease Accuracy of Top 30 Metabolites in the Random Forest Analysis. Metabolites that caused the greatest change in the random forest analysis are positioned from top to bottom with percentage change in the X axis. 4A: Mean decrease accuracy in those who developed ACLF 4B: Mean decrease accuracy in those who developed inpatient death 4C: Mean decrease accuracy in those who died within 30 days

Figure 5:. Correlation networks between metabolites and stool mtcrobiota

Correlation networks in patients reaching outcomes filtered at r>0.7/r<−0.7 and p<0.001 between microbiota (red nodes) and metabolites (green nodes). Red edges indicate negative while blue indicate positive correlation. 5A-C: Sub-networks of important taxa in those who developed ACLF 5A: Fusobacteriaceae and Enterococcaceae showed positive linkages with CMPF and negative with phospholipids, while the reverse was seen with Lachnospiraceae 5B: Clostridium cluster XI showed positive linkages with aromatic compounds 5C. Veillonellaceae, Porphyromonadaceae linked to compounds protective against ACLF 5D-E: Sub-networks of important taxa in those who died as an inpatient. 5D: Enterococcaceae associated with sex steroids and benzoate compounds associated with death, while Ruminococcaceae and Lachnospiraceaeae are positively linked with glycoursodeoxycholate, which is protective. 5E: Clostridiaceae and Peptostreptococcaceae are negatively linked with CMPF/hydoxy-CMPF, which in turn are negatively linked with protective glycoursodeoxycholate 5F: 30-day death sub-network showed Clostridium cluster XI linked positively with phenylacetylglutamine, which is associated with death and negatively with commensal Bacteroidaceae, which in turn are negatively correlated with pathobionts. Pasteurellaceae are negatively linked with phospholipids and Commamondaceae negatively with protective ursodeoxycholate.