Sikaroodi, Masoumeh

Background and Aims: Prognosticating outcomes such as hospitalizations in outpatients with cirrhosis is challenging, especially with changing etiologies and demographics. This study aims to determine the impact of multi-omic strategies on outcome prediction. Approach and Results: NACSELD3 enrolls outpatients with cirrhosis with controlled/eradicated etiologies from 10 centers and follows them systematically. At baseline, clinical/demographic and cirrhosis details were recorded and saliva and serum samples were collected for microbiome and metabolome analysis, respectively. Multi-omic bioinformatic studies to determine the interaction of microbiota and metabolites with the clinical prediction of 6-month hospitalizations were performed. Five hundred sixty-five patients (60.2 y, 68% men, 35% alcohol, 33% metabolic dysfunction-associated steatohepatitis, 21%, eradicated HCV with MELD 3.0 12) were enrolled. One hundred sixty-three (29%) required 6-month hospitalizations; most (75%) were liver-related. Those hospitalized had worse cirrhosis severity and comorbidity indices but similar demographics and oral health variables. Salivary microbiome alpha-diversity was lower (1.96±0.48 vs. 2.09±0.45, p =0.018) with greater pathobionts ( Streptococcus , Treponema, Enterococcaceae) and lower commensal genhospitalized/noera ( Veillonella, Prevotella, Haemophilus , Lachnospiraceae spp) at baseline. Serum metabolomics showed significant separation at baseline between hospitalized/non-hospitalized patients using supervised analyses with microbial-origin (phenyllactate, secondary bile acids, indoles), choline moieties, and polyamine/GABA (3-ureidopropionate/spermidine) metabolites being most prominent. Area under the curve using random forest for clinical, microbial, and metabolomic variables was higher than that of these individually. Latent factor analysis showed clinical variables (MELD 3.0, hemoglobin, and albumin) with the greatest impact, followed by salivary microbiota and then serum microbiome for hospitalization prediction. Conclusions: In a multicenter North American outpatient cirrhosis cohort with controlled etiologies, serum metabolomics and salivary microbiome add to clinical variables to prognosticate 6-month hospitalization.

Abstract Cirrhosis is complicated by a high rate of nosocomial infections (NIs), which result in poor outcomes and are challenging to predict using clinical variables alone. Our aim was to determine predictors of NI using admission serum metabolomics and gut microbiota in inpatients with cirrhosis. In this multicenter inpatient cirrhosis study, serum was collected on admission for liquid chromatography–mass spectrometry metabolomics, and a subset provided stool for 16SrRNA analysis. Hospital course, including NI development and death, were analyzed. Metabolomic analysis using analysis of covariance (ANCOVA) (demographics, Model for End‐Stage Liver Disease [MELD] admission score, white blood count [WBC], rifaximin, and infection status adjusted) and random forest analyses for NI development were performed. Additional values of serum metabolites over clinical variables toward NI were evaluated using logistic regression. Stool microbiota and metabolomic correlations were compared in patients with and without NI development. A total of 602 patients (231 infection admissions) were included; 101 (17%) developed NIs, which resulted in worse inpatient outcomes, including intensive care unit transfer, organ failure, and death. A total of 127 patients also gave stool samples, and 20 of these patients developed NIs. The most common NIs were spontaneous bacterial peritonitis followed by urinary tract infection, Clostridioides difficile, and pneumonia. A total of 247 metabolites were significantly altered on ANCOVA. Higher MELD scores (odds ratio, 1.05; p < 0.0001), admission infection (odds ratio, 3.54; p < 0.0001), and admission WBC (odds ratio, 1.05; p = 0.04) predicted NI (area under the curve, 0.74), which increased to 0.77 (p = 0.05) with lower 1‐linolenoyl‐glycerolphosphocholine (GPC) and 1‐stearoyl‐GPC and higher N‐acetyltryptophan and N‐acetyl isoputreanine. Commensal microbiota were lower and pathobionts were higher in those who developed NIs. Microbial–metabolite correlation networks were complex and dense in patients with NIs, especially sub‐networks centered on Ruminococcaceae and Pseudomonadaceae. NIs are common and associated with poor outcomes in cirrhosis. Admission gut microbiota in patients with NIs showed higher pathobionts and lower commensal microbiota. Microbial–metabolomic correlations were more complex, dense, and homogeneous among those who developed NIs, indicating greater linkage strength. Serum metabolites and gut microbiota on admission are associated with NI development in cirrhosis.

Background and Aims The role of the intestinal microbiome in alcoholic hepatitis is not established. The aims of this study were to (1) characterize the fecal microbial ecology associated with alcoholic hepatitis, (2) relate microbiome changes to disease severity, and (3) infer the functional relevance of shifts in microbial ecology. Approach and Results The fecal microbiome in patients with moderate alcoholic hepatitis (MAH) or severe alcoholic hepatitis (SAH) was compared with healthy controls (HCs) and heavy drinking controls (HDCs). Microbial taxa were identified by 16S pyrosequencing. Functional metagenomics was performed using PICRUSt. Fecal short chain fatty acids (SCFAs) were measured using a liquid chromatography–mass spectrometry platform. A total of 78 participants (HC, n = 24; HDC, n = 20; MAH, n = 10; SAH, n = 24) were studied. HDC had a distinct signature compared with HC with depletion of Bacteroidetes (46% vs. 26%; P = 0.01). Alcoholic hepatitis was associated with a distinct microbiome signature compared with HDC (area under the curve = 0.826); differential abundance of Ruminococcaceae , Veillonellaceae , Lachnospiraceae , Porphyromonadaceae , and Rikenellaceae families were the key contributors to these differences. The beta diversity was significantly different among the groups (permutational multivariate analysis of variance [PERMANOVA] P < 0.001). SAH was associated with increased Proteobacteria (SAH 14% vs. HDC 7% and SAH vs. HC 2%, P = 0.20 and 0.01, respectively). Firmicutes abundance declined from HDC to MAH to SAH (63% vs. 53% vs. 48%, respectively; P = 0.09, HDC vs. SAH). Microbial taxa did not distinguish between MAH and SAH (PERMANOVA P = 0.785). SCFAs producing bacteria (Lachnospiraceae and Ruminococcaceae) were decreased in alcoholic hepatitis, and a similar decrease was observed in fecal SCFAs among alcoholic hepatitis patients. Conclusions There are distinct changes in fecal microbiome associated with the development, but not severity, of alcoholic hepatitis.