Shah, Vijay H.

Background and Aims: In a recent trial, patients with severe alcohol-associated hepatitis treated with anakinra plus zinc (A+Z) had lower survival and higher acute kidney injury (AKI) rates versus prednisone (PRED). We characterize the clinical factors and potential mechanisms associated with AKI development in that trial. Approach and Results: Data from 147 participants in a multicenter randomized clinical trial (74 A+Z, 73 PRED) were analyzed. AKI, AKI phenotypes, and kidney injury biomarkers were compared between participants who did/did not develop AKI in the 2 treatment arms. Multivariable competing risk analyses were performed to identify baseline risk factors for incident AKI, with death treated as a competing event. Risk factors considered were age, sex, mean arterial pressure, white blood cell count, albumin, MELD, ascites, HE, and treatment arm. At baseline, no participants had AKI; 33% (n=49) developed AKI during follow-up. AKI incidence was higher in A+Z than in PRED (45% [n=33] versus 22% [n=16], p=0.001). AKI phenotypes were similar between the 2 treatment arms (p=0.361), but peak AKI severity was greater in A+Z than PRED (stage 3 n=21 [63.6%] vs. n=8 [50.0%], p=0.035). At baseline, urine-neutrophil-gelatinase–associated lipocalin levels were similar between participants who developed AKI in both treatment arms (p=0.319). However, day 7 and 14 urine-neutrophil-gelatinase–associated lipocalin levels were significantly elevated in participants treated with A+Z who developed AKI versus participants treated with PRED who developed AKI (p=0.002 and 0.032, respectively). On multivariable competing risk analysis, only A+Z was independently associated with incident AKI (subdistribution hazard ratio 2.35, p=0.005). Conclusions: AKI occurred more frequently and was more severe in participants treated with A+Z. A+Z–treated participants with AKI had higher urine-neutrophil-gelatinase–associated lipocalin, suggesting that A+Z maybe nephrotoxic in patients with severe alcohol-associated hepatitis.

INTRODUCTION: One of the primary goals of the Liver Cirrhosis Network (LCN) is to develop a cohort study to better understand and predict the risk of hepatic decompensation and other clinical and patient-reported outcomes among patients with Child A cirrhosis. METHODS: The LCN consists of a Scientific Data Coordinating Center and 10 clinical centers whose investigators populate multiple committees. The LCN Definitions and Measurements Committee developed preliminary definitions of cirrhosis and its complications by literature review, expert opinion, and reviewing definition documents developed by other organizations. The Cohort Committee developed the study protocol with the input of the steering committee. RESULTS: The LCN developed a prospective cohort study to describe and predict the rates of incident clinical events pertaining to first decompensation and patient-reported outcomes. The LCN developed a pragmatic definition of compensated cirrhosis incorporating clinical, laboratory, imaging, and histological criteria. Definitions of incident and recompensated ascites, overt hepatic encephalopathy, variceal hemorrhage, bleeding because of portal gastropathy, and hepatocellular carcinoma were also codified. DISCUSSION: The LCN Cohort Study design will inform the natural history of cirrhosis in contemporary patients with compensated cirrhosis. The LCN Definitions and Measures Committee developed criteria for the definition of cirrhosis to standardize entry into this multicenter cohort study and standardized criteria for liver-related outcome measures. This effort has produced definitions intended to be both sensitive and specific as well as easily operationalized by study staff such that outcomes critical to the LCN cohort are identified and reported in an accurate and generalizable fashion. REGISTRATION: NCT05740358.

Liver fibrosis is characterized by the activation of perivascular hepatic stellate cells (HSCs), the release of fibrogenic nanosized extracellular vesicles (EVs), and increased HSC glycolysis. Nevertheless, how glycolysis in HSCs coordinates fibrosis amplification through tissue zone-specific pathways remains elusive. Here, we demonstrate that HSC-specific genetic inhibition of glycolysis reduced liver fibrosis. Moreover, spatial transcriptomics revealed a fibrosis-mediated up-regulation of EV-related pathways in the liver pericentral zone, which was abrogated by glycolysis genetic inhibition. Mechanistically, glycolysis in HSCs up-regulated the expression of EV-related genes such as Ras-related protein Rab-31 ( RAB31 ) by enhancing histone 3 lysine 9 acetylation on the promoter region, which increased EV release. Functionally, these glycolysis-dependent EVs increased fibrotic gene expression in recipient HSC. Furthermore, EVs derived from glycolysis-deficient mice abrogated liver fibrosis amplification in contrast to glycolysis-competent mouse EVs. In summary, glycolysis in HSCs amplifies liver fibrosis by promoting fibrogenic EV release in the hepatic pericentral zone, which represents a potential therapeutic target.

The rapid evolution of artificial intelligence and the widespread embrace of digital technologies have ushered in a new era of clinical research and practice in hepatology. Although its potential is far from realization, these significant strides have generated new opportunities to address existing gaps in the delivery of care for patients with liver disease. In this review, we discuss how artificial intelligence and opportunities for multimodal data integration can improve the diagnosis, prognosis, and management of alcohol-associated liver disease. An emphasis is made on how these approaches will also benefit the detection and management of alcohol use disorder. Our discussion encompasses challenges and limitations, concluding with a glimpse into the promising future of these advancements.

Portal hypertension (PHTN) is a severe complication of liver cirrhosis and is associated with intrahepatic sinusoidal remodeling induced by sinusoidal resistance and angiogenesis. Collagen type IV (COL4), a major component of basement membrane, forms in liver sinusoids upon chronic liver injury. However, the role, cellular source, and expression regulation of COL4 in liver diseases are unknown. Here, we examined how COL4 is produced and how it regulates sinusoidal remodeling in fibrosis and PHTN. Human cirrhotic liver sample RNA sequencing showed increased COL4 expression, which was further verified via immunofluorescence staining. Single-cell RNA sequencing identified liver sinusoidal endothelial cells (LSECs) as the predominant source of COL4 upregulation in mouse fibrotic liver. In addition, COL4 was upregulated in a TNF-α/NF-κB–dependent manner through an epigenetic mechanism in LSECs in vitro. Indeed, by utilizing a CRISPRi-dCas9-KRAB epigenome-editing approach, epigenetic repression of the enhancer-promoter interaction showed silencing of COL4 gene expression. LSEC-specific COL4 gene mutation or repression in vivo abrogated sinusoidal resistance and angiogenesis, which thereby alleviated sinusoidal remodeling and PHTN. Our findings reveal that LSECs promote sinusoidal remodeling and PHTN during liver fibrosis through COL4 deposition.

Background and Aims: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence in a global cohort, we sought to derive and validate an enhanced prognostic model. Approach and Results: The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled patients with AH per National Institute for Alcohol Abuse and Alcoholism criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day postadmission mortality, 3 artificial intelligence algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined through Bayesian updating, integrating the derivation cohort’s average 90-day mortality with each center’s approximate mortality rate to produce posttest probabilities. The ALCoholic Hepatitis Artificial INtelligence Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30 d) and 27.9% (90 d) in the derivation cohort versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779–0.844) and 0.799 (0.769–0.830), significantly surpassing legacy models like Maddrey’s Discriminant Function, Model for End-Stage Liver Disease variations, age-serum bilirubin-international normalized ratio-serum Creatinine score, Glasgow, and modified Glasgow Scores (p < 0.001). ALCoholic Hepatitis Artificial INtelligence Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCoholic Hepatitis Artificial INtelligence Ensemble score > 0.20 in both derivation and validation cohorts. Conclusions: Harnessing artificial intelligence within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it’s accessible at: https://aihepatology.shinyapps.io/ALCHAIN/.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction–associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction–associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Background and Aims: Transforming growth factor-beta 1 (TGFβ1) induces HSC activation into metastasis-promoting cancer-associated fibroblasts (CAFs), but how the process is fueled remains incompletely understood. We studied metabolic reprogramming induced by TGFβ1 in HSCs. Approaches and Results: Activation of cultured primary human HSCs was assessed by the expression of myofibroblast markers. Glucose transporter 1 (Glut1) of murine HSC was disrupted by Cre recombinase/LoxP sequence derived from bacteriophage P1 recombination (Cre/LoxP). Plasma membrane (PM) Glut1 and glycolysis were studied by biotinylation assay and the Angilent Seahorse XFe96 Analyzer. S.c. HSC/tumor co-implantation and portal vein injection of MC38 colorectal cancer cells into HSC-specific Glut1 knockout mice were performed to determine in vivo relevance. Transcriptome was obtained by RNA sequencing of HSCs and spatialomics with MC38 liver metastases. TGFβ1-induced CAF activation of HSCs was accompanied by elevation of PM Glut1, glucose uptake, and glycolysis. Targeting Glut1 or Src by short hairpin RNA, pharmacologic inhibition, or a Src SH3 domain deletion mutant abrogated TGFβ1-stimulated PM accumulation of Glut1, glycolysis, and CAF activation. Mechanistically, binding of the Src SH3 domain to SH3 domain–binding protein 5 led to a Src/SH3 domain–binding protein 5/Rab11/Glut1 complex that activated Rab11-dependent Glut1 PM transport under TGFβ1 stimulation. Deleting the Src SH3 domain or targeting Glut1 of HSCs by short hairpin RNA or Cre recombinase/LoxP sequence derived from bacteriophage P1 recombination suppressed CAF activation in mice and MC38 colorectal liver metastasis. Multi-omics revealed that Glut1 deficiency in HSCs/CAFs suppressed HSC expression of tumor-promoting factors and altered MC38 transcriptome, contributing to reduced MC38 liver metastases. Conclusion: The Src SH3 domain–facilitated metabolic reprogramming induced by TGFβ1 represents a target to inhibit CAF activation and the pro-metastatic liver microenvironment.