Epidemiology of liver diseases: global disease burden and forecasted research trends.

We assessed the global incidence, mortality, and disability-adjusted life years (DALYs) associated with various liver diseases, including alcohol-related liver disease (ALD), hepatitis B/C virus infections (HBV or HCV), liver cancer, metabolic dysfunction-associated steatotic liver disease (MASLD), and other chronic liver diseases, from the 2019 Global Burden of Disease study. Additionally, we analyzed the global trends in hepatology research and drug development. From 2000 to 2019, prevalence rates increased for ALD, MASLD and other liver diseases, while they decreased for HBV, HCV, and liver cancer. Countries with a high socio-demographic index (SDI) exhibited the lowest mortality rates and DALYs. The burden of liver diseases varied due to factors like sex and region. In nine representative countries, MASLD, along with hepatobiliary cancer, showed highest increase in funding in hepatology research. Globally, the major research categories in hepatology papers from 2000 to 2019 were cancer, pathobiology, and MASLD. The United States (U.S.) was at the forefront of hepatology research, with China gradually increasing its influence over time. Hepatologists worldwide are increasingly focusing on studying the communication between the liver and other organs, while underestimating the research on ALD. Cancer, HCV, and MASLD were the primary diseases targeted for therapeutic development in clinical trials. However, the proportion of new drugs approved for the treatment of liver diseases was relatively low among all newly approved drugs in the U.S., China, Japan, and the European Union. Notably, there were no approved drug for the treatment of ALD in the world.

Artificial Intelligence, Large Language Models, and Digital Health in the Management of Alcohol-Associated Liver Disease.

Artificial intelligence (AI) has the potential to aid in the diagnosis and management of alcohol-associated liver disease (ALD). Machine learning algorithms can analyze medical data, such as patient records and imaging results, to identify patterns and predict disease progression. Newer advances such as large language models (LLMs) can enhance early detection and personalized treatment strategies for individuals with chronic diseases such as ALD. However, it is essential to integrate LLMs and other AI tools responsibly, considering ethical concerns in health care applications and ensuring an evidence base for real-world applications of the existing knowledge.

Performance of AI-Enabled Electrocardiogram in the Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease.

BACKGROUND AND AIMS: Accessible noninvasive screening tools for metabolic dysfunction-associated steatotic liver disease (MASLD) are needed. We aim to explore the performance of a deep learning-based artificial intelligence (AI) model in distinguishing the presence of MASLD using 12-lead electrocardiogram (ECG). METHODS: This is a retrospective study of adults diagnosed with MASLD in Olmsted County, Minnesota, between 1996 and 2019. Both cases and controls had ECGs performed within 6 years before and 1 year after study entry. An AI-based ECG model using a convolutional neural network was trained, validated, and tested in 70%, 10%, and 20% of the cohort, respectively. External validation was performed in an independent cohort from Mayo Clinic Enterprise. The primary outcome was the performance of ECG to identify MASLD, alone or when added to clinical parameters. RESULTS: A total of 3468 MASLD cases and 25,407 controls were identified. The AI-ECG model predicted the presence of MASLD with an area under the curve (AUC) of 0.69 (original cohort) and 0.62 (validation cohort). The performance was similar or superior to age- and sex-adjusted models using body mass index (AUC, 0.71), presence of diabetes, hypertension or hyperlipidemia (AUC, 0.68), or diabetes alone (AUC, 0.66). The model combining ECG, age, sex, body mass index, diabetes, and alanine aminotransferase had the highest AUC: 0.76 (original) and 0.72 (validation). CONCLUSIONS: This is a proof-of-concept study that an AI-based ECG model can detect MASLD with a comparable or superior performance as compared with the models using a single clinical parameter but not superior to the combination of clinical parameters. ECG can serve as another screening tool for MASLD in the nonhepatology space.

Racial and ethnic disparities in the natural history of alcohol-associated liver disease in the United States.

BACKGROUND: Outcomes in alcohol-associated liver disease (ALD) are influenced by several race and ethnic factors, yet its natural history across the continuum of patients in different stages of the disease is unknown. METHODS: We conducted a retrospective cohort study of U.S. adults from 2011 to 2018, using three nationally representative databases to examine potential disparities in relevant outcomes among racial and ethnic groups. Our analysis included logistic and linear regressions, along with competing risk analysis. RESULTS: Black individuals had the highest daily alcohol consumption (12.6 g/day) while Hispanic participants had the largest prevalence of heavy episodic drinking (33.5%). In a multivariable-adjusted model, Hispanic and Asian participants were independently associated with a higher ALD prevalence compared to Non-Hispanic White interviewees (OR: 1.4, 95% CI: 1.1-1.8 and OR: 1.5 95% CI:1.1-2.0, respectively), while Blacks participants had a lower ALD prevalence (OR: .7 95% CI: .6-.9), and a lower risk of mortality during hospitalization due to ALD (OR: .83 95% CI: .73-.94). Finally, a multivariate competing-risk analysis showed that Hispanic ethnicity had a decreased probability of liver transplantation if waitlisted for ALD (SHR: .7, 95% CI: .6-.8) along with female Asian population (HR: .40, 95% CI: .26-.62). CONCLUSIONS: After accounting for key social and biological health determinants, the Hispanic population showed an increased risk of ALD prevalence, even with lower alcohol consumption. Additionally, Hispanic and Asian female patients had reduced access to liver transplantation compared to other enlisted patients.

Analysis and Validation of Image Search Engines in Histopathology.

Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient tissue comparison for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient tissue comparison. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets (1269 patients) and three public datasets (1207 patients), totaling more than 200, 000 patches from 38 different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.

Liver stiffness measurement by magnetic resonance elastography predicts cirrhosis and decompensation in alcohol-related liver disease.

PURPOSE: To evaluate magnetic resonance elastography (MRE)-based liver stiffness measurement as a biomarker to predict the onset of cirrhosis in early-stage alcohol-related liver disease (ALD) patients, and the transition from compensated to decompensated cirrhosis in ALD. METHODS: Patients with ALD and at least one MRE examination between 2007 and 2020 were included in this study. Patient demographics, liver chemistries, MELD score (within 30 days of the first MRE), and alcohol abstinence history were collected from the electronic medical records. Liver stiffness and fat fraction were measured. Disease progression was assessed in the records by noting cirrhosis onset in early-stage ALD patients and decompensation in those initially presenting with compensated cirrhosis. Nomograms and cut-off values of liver stiffness, derived from Cox proportional hazards models were created to predict the likelihood of advancing to cirrhosis or decompensation. RESULTS: A total of 182 patients (132 men, median age 57 years) were included in this study. Among 110 patients with early-stage ALD, 23 (20.9%) developed cirrhosis after a median follow-up of 6.2 years. Among 72 patients with compensated cirrhosis, 33 (45.8%) developed decompensation after a median follow-up of 4.2 years. MRE-based liver stiffness, whether considered independently or adjusted for age, alcohol abstinence, fat fraction, and sex, was a significant and independent predictor for both future cirrhosis (Hazard ratio [HR] = 2.0-2.2, p = 0.002-0.003) and hepatic decompensation (HR = 1.2-1.3, p = 0.0001-0.006). Simplified Cox models, thresholds, and corresponding nomograms were devised for practical use, excluding non-significant or biased variables. CONCLUSIONS: MRE-based liver stiffness assessment is a useful predictor for the development of cirrhosis or decompensation in patients with ALD.

The Liver Cirrhosis Network Cohort Study: Cirrhosis Definition, Study Population, and Endpoints.

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.

Design, implementation, and impact of a cirrhosis-specific remote patient monitoring program.

BACKGROUND: Remote patient monitoring (RPM) is an emerging focus in health care, and specialized programs may reduce medical costs, supplement in-office visits, and improve patient satisfaction. In this study, we describe the development, feasibility, and early outcomes of an RPM program for patients with decompensated cirrhosis. METHODS: Forty-six patients were offered enrollment at the time of hospital discharge in the cirrhosis RPM program (CiRPM), of which 41 completed at least 30 days of monitoring. Participants were mailed remote monitoring equipment and a tablet to be used for patient-reported outcomes. Alerts were continuously monitored by virtual nursing staff who could perform targeted interventions. A cohort of historical controls (n = 74) was created for comparison using inverse probability of treatment weighting. RESULTS: Patients were enrolled in the program for a mean of 83.9 days, with 28 (68%) completing the full 90-day program. Participants uploaded vital signs and responded to symptom-based questionnaires on 93% of the monitored days. On end-of-program surveys, over 75% of patients expressed satisfaction with the program. Gender, age, and MELD-Na were similar between CiRPM and weighted control groups. The 90-day readmission rate was 34% in CiRPM and 47% in weighted controls. In the CiRPM group, 12% of subjects had 2 or more admissions, compared to 37% in the weighted control group. CONCLUSION: This study demonstrates the feasibility of a cirrhosis-specific RPM program. Overall, patient satisfaction and utilization of the CiRPM was high. Future studies are needed to confirm the impact of RPM on the reduction of hospital readmissions in decompensated cirrhosis.

Acute kidney injury in severe alcohol-associated hepatitis treated with anakinra plus zinc or prednisone.

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.

Glycolysis in hepatic stellate cells coordinates fibrogenic extracellular vesicle release spatially to amplify liver fibrosis.

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 Pathophysiology of Portal Hypertension.

This article reviews the pathophysiology of portal hypertension that includes multiple mechanisms internal and external to the liver. This article starts with a review of literature describing the cellular and molecular mechanisms of portal hypertension, microvascular thrombosis, sinusoidal venous congestion, portal angiogenesis, vascular hypocontractility, and hyperdynamic circulation. Mechanotransduction and the gut-liver axis, which are newer areas of research, are reviewed. Dysfunction of this axis contributes to chronic liver injury, inflammation, fibrosis, and portal hypertension. Sequelae of portal hypertension are discussed in subsequent studies.

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.

Angiocrine signaling in sinusoidal homeostasis and liver diseases.

The hepatic sinusoids are composed of liver sinusoidal endothelial cells (LSECs), which are surrounded by hepatic stellate cells (HSCs) and contain liver-resident macrophages called Kupffer cells, and other patrolling immune cells. All these cells communicate with each other and with hepatocytes to maintain sinusoidal homeostasis and a spectrum of hepatic functions under healthy conditions. Sinusoidal homeostasis is disrupted by metabolites, toxins, viruses, and other pathological factors, leading to liver injury, chronic liver diseases, and cirrhosis. Alterations in hepatic sinusoids are linked to fibrosis progression and portal hypertension. LSECs are crucial regulators of cellular crosstalk within their microenvironment via angiocrine signaling. This review discusses the mechanisms by which angiocrine signaling orchestrates sinusoidal homeostasis, as well as the development of liver diseases. Here, we summarise the crosstalk between LSECs, HSCs, hepatocytes, cholangiocytes, and immune cells in health and disease and comment on potential novel therapeutic methods for treating liver diseases.

Liver sinusoidal endothelial cells contribute to portal hypertension through collagen type IV-driven sinusoidal remodeling.

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.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

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.