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.

Background: The accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured “mock” datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation as well as its impact on identifying taxa that discriminate between disease states.Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where the same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs while preserving the between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between the filtering and the contaminant removal method shows that they have complementary effects and are advised to be used in conjunction.Conclusions: Filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

Background and Aims Utilization and safety of cannabidiol (CBD) in patients with autoimmune hepatitis (AIH) are currently unknown. We aimed to identify the frequency of CBD use, impact on symptoms, and safety profile. Methods An invitation to complete a CBD-specific questionnaire was posted every other day to well-established autoimmune hepatitis Facebook communities (combined membership of 2600 individuals) during a 10-day study period. Age ≥ 18 years and an AIH diagnosis by a physician were the eligibility criteria for participation in the survey. Results In total, 371 AIH patients (median age 49 years, 32% reported advanced fibrosis) completed the questionnaire. Respondents were 91% women, 89% Caucasian, and 89% from North America. Ninety-three (25%) respondents were ever CBD users, with 55 of them (15% of the survey responders) identified as current users. Among ever users, 45.7% reported their treating doctors were aware of their CBD use. The most common reason cited for CBD use was pain (68%), poor sleep (62%), and fatigue (38%). Most respondents using CBD for these symptoms reported a significant improvement in pain (82%), sleep (87%), and fatigue (61%). In ever CBD users, 17.3% were able to stop a prescription medication because of CBD use: pain medication (47%), immunosuppression (24%), and sleep aids (12%). Side effects attributed to CBD use were reported in 3% of CBD users, yet there were no reported emergency department visits or hospitalizations. Conclusion CBD use was not uncommon in patients with AIH, and its use was associated with reports of improvement in extrahepatic symptoms.

The management of patients with autoimmune hepatitis (AIH) in the era of SARS-CoV-2 is challenging given minimal published clinical data. We used a large cohort of patients with AIH across the USA to investigate the differences in known risk factors for severe SARS-CoV-2 and AIH characteristics among patients who experienced symptoms consistent with COVID-19 illness versus those who did not. Additionally, we explored the effect of living through the SARS-CoV-2 pandemic on the extrahepatic symptoms and behaviors of patients with AIH. An invitation to complete a COVID-19-specific questionnaire was publicized in well-established social media cohorts of patients with AIH. Eligibility criteria were age ≥18 years, US residency, and an AIH diagnosis by a physician. A total of 420 individuals were eligible for the study. Symptoms consistent with COVID-19 were reported in 11% (n=48) with 3 patients requiring hospitalizations. Body mass index (BMI) >40 kg/m2 (23% vs 10%, p=0.01) and exposure to house (33% vs 3%, p=0.0001) or work (38% vs 17%, p=0.02) contacts with COVID-19 were factors found higher in those with symptoms. Cirrhosis or steroid use or immunosuppression was not significantly different between symptomatic and non-symptomatic groups. Worsening fatigue (45% vs 30%, p=0.06), anxiety (89% vs 70%, p=0.08), and itch (40% vs 18%, p=0.03) were more common among those reporting COVID-19 symptoms compared with those without. BMI >40 kg/m2 and exposure to contacts with COVID-19 illness but not cirrhosis or immunosuppression were associated with increased risk of COVID-19 illness in patients with AIH.

Background Significant reduction in quality of life among patients with autoimmune hepatitis (AIH) patients has been observed in several studies. While acute symptoms associated with AIH have been well described, little is known about the overall impact of living with AIH on patients’ quality of life. The aim of this qualitative descriptive study was to describe the impact of AIH and associated symptoms on quality of life from the perspectives of patients living with AIH. Methods Patients from Autoimmune Hepatitis Association support groups were recruited to participate in one of five online focus groups conducted between August and September 2020. After enrollment, patients were asked to complete a brief demographic and disease history questionnaire. A single moderator conducted interviews with each group guided by seven questions focused on the impact of AIH on the participants’ quality of life. Each session was recorded, transcribed, and verified. Content analysis was used to summarize the participants’ responses. Results The participants’ discussed three overarching topics: (a) symptoms of AIH and medication side effects, (b) the impact the disease and symptoms/side effects on five domains of quality of life (work life, relationships with friends and family, social life, leisure activities, and diet and exercise) and (c) interactions with healthcare providers and recommendations for future research. Conclusions Living with AIH can have profound effects on patients’ quality of life in several domains. Healthcare providers and the AIH research community should focus on developing further strategies that can improve the quality of life in persons suffering from AIH.