Machine Learning Applied to Omics Datasets Predicts Mortality in Patients with Alcoholic Hepatitis

Insights into ALD and AUD diagnosis and prognosis: Exploring AI and multimodal data streams

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.

Gut-liver axis: Recent concepts in pathophysiology in alcohol-associated liver disease

The growing recognition of the role of the gut microbiome's impact on alcohol-associated diseases, especially in alcohol-associated liver disease, emphasizes the need to understand molecular mechanisms involved in governing organ-organ communication to identify novel avenues to combat alcohol-associated diseases. The gut-liver axis refers to the bidirectional communication and interaction between the gut and the liver. Intestinal microbiota plays a pivotal role in maintaining homeostasis within the gut-liver axis, and this axis plays a significant role in alcohol-associated liver disease. The intricate communication between intestine and liver involves communication between multiple cellular components in each organ that enable them to carry out their physiological functions. In this review, we focus on novel approaches to understanding how chronic alcohol exposure impacts the microbiome and individual cells within the liver and intestine, as well as the impact of ethanol on the molecular machinery required for intraorgan and interorgan communication.

Comprehensive bibliometric and visualized analysis of research on gut-liver axis published from 1998 to 2022

Background

The concept of the gut-liver axis was proposed by Marshall in 1998, and since then, this hypothesis has been gradually accepted by the academic community. Many publications have been published on the gut-liver axis, making it important to assess the scientific implications of these studies and the trends in this field.

Methods

Publications were retrieved from the Web of Science Core Collection. Microsoft Excel, CiteSpace, VOSviewer, and Scimago Graphica software were used for bibliometric analysis.

Results

A total of 776 publications from the Web of Science core database were included in this study. In the past 25 years, the number of publications on the gut-liver axis has shown an upward trend, particularly in the past 3 years (2020-2022). China had the highest number of publications (267 articles, 34.4%). However, the United States was at the top regarding influence and international cooperation in this field. The University of California San Diego had contributed the most publications. Suk, Ki Tae and Schnabl, Bernd were tied for the first rank in most publications. Thematic hotspots and frontiers were focused on gut microbiota, microbial metabolite, intestinal permeability, bacterial translocation, bile acid, non-alcoholic steatohepatitis, and alcoholic liver disease.

Conclusion

Our study is the first bibliometric analysis of literature using visualization software to present the current research status of the gut-liver axis over the past 25 years. The damage and repair of intestinal barrier function, as well as the disruption of gut microbiota and host metabolism, should be a focus of attention. This study can provide a reference for later researchers to understand the global research trends, hotspots, and frontiers in this field.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Gut-liver axis: Pathophysiological concepts and clinical implications

Bidirectional crosstalk along the gut-liver axis controls gastrointestinal health and disease and exploits environmental and host mediators. Nutrients, microbial antigens, metabolites, and bile acids regulate metabolism and immune responses in the gut and liver, which reciprocally shape microbial community structure and function. Perturbation of such host-microbe interactions is observed in a variety of experimental liver diseases and is facilitated by an impaired intestinal barrier, which is fueling hepatic inflammation and disease progression. Clinical evidence describes perturbation of the gut-liver crosstalk in non-alcoholic fatty liver disease, alcoholic liver disease, and primary sclerosing cholangitis. In liver cirrhosis, a common sequela of these diseases, the intestinal microbiota and microbial pathogen-associated molecular patterns constitute liver inflammation and clinical complications, such as hepatic encephalopathy. Understanding the intricate metabolic interplay between the gut and liver in health and disease opens an avenue for targeted therapies in the future, which is probed in controlled clinical trials.

Machine learning algorithms based on proteomic data mining accurately predicting the recurrence of hepatitis B-related hepatocellular carcinoma

BACKGROUND AND AIM

Over 10% of hepatocellular carcinoma (HCC) cases recur each year, even after surgical resection. Currently, there is a lack of knowledge about the causes of recurrence and the effective prevention. Prediction of HCC recurrence requires diagnostic markers endowed with high sensitivity and specificity. This study aims to identify new key proteins for HCC recurrence and to build machine learning algorithms for predicting HCC recurrence.

METHODS

The proteomics data for analysis in this study were obtained from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database. We analyzed different proteins based on cases with or without recurrence of HCC. Survival analysis, Cox regression analysis, and area under the ROC curves (AUROC > 0.7) were used to screen for more significant differential proteins. Predictive models for HCC recurrence were developed using four machine learning algorithms.

RESULTS

A total of 690 differentially expressed proteins between 50 relapsed and 77 non-relapsed hepatitis B-related HCC patients were identified. Seven of these proteins had an AUROC > 0.7 for 5-year survival in HCC, including BAHCC1, ESF1, RAP1GAP, RUFY1, SCAMP3, STK3, and TMEM230. Among the machine learning algorithms, the random forest algorithm showed the highest AUROC values (AUROC: 0.991, 95% CI 0.962-0.999) for identifying HCC recurrence, followed by the support vector machine (AUROC: 0.893, 95% Cl 0.824-0.956), the logistic regression (AUROC: 0.774, 95% Cl 0.672-0.868), and the multi-layer perceptron algorithm (AUROC: 0.571, 95% Cl 0.459-0.682).

CONCLUSIONS

Our study identifies seven novel proteins for predicting HCC recurrence and the random forest algorithm as the most suitable predictive model for HCC recurrence.

Role of gut bacterial and non-bacterial microbiota in alcohol-associated liver disease: Molecular mechanisms, biomarkers, and therapeutic prospective

Alcohol-associated liver disease (ALD) comprises a spectrum of liver diseases that include: steatosis to alcohol-associated hepatitis, cirrhosis, and ultimately hepatocellular carcinoma. The pathophysiology and potential underlying mechanisms for alcohol-associated liver disease are unclear. Moreover, the treatment of ALD remains a challenge. Intestinal microbiota include bacteria, fungi, and viruses, that are now known to be important in the development of ALD. Alcohol consumption can change the gut microbiota and function leading to liver disease. Given the importance of interactions between intestinal microbiota, alcohol, and liver injury, the gut microbiota has emerged as a potential biomarker and therapeutic target. This review focuses on the potential mechanisms by which the gut microbiota may be involved in the pathogenesis of ALD and explains how this can be translated into clinical management. We discuss the potential of utilizing the gut microbiota signature as a biomarker in ALD patients. Additionally, we present an overview of the prospect of modulating the intestinal microbiota for the management of ALD.