Emerging synthetic drugs for the treatment of hepatic cirrhosis: a 2024 update

Cirrhosis is associated with a substantial, and increasing, health and socioeconomic burden worldwide. The leading causes of cirrhosis are steatotic liver diseases (including alcohol-related liver disease and metabolic dysfunction-associated steatohepatitis (MASH, formerly nonalcoholic steatohepatitis (NASH)) and chronic viral hepatitis. Owing to rising rates of alcohol consumption, an aging general population, and a spiralling prevalence of metabolic risk factors, a dramatic escalation in cirrhosis-related morbidity and mortality is predicted [1]. It currently ranks tenth among the leading causes of death in Africa (compared to thirteenth in 2015), ninth in Southeast Asia and Europe, and fifth in the Eastern Mediterranean [1]. Cirrhosis is associated with huge healthcare costs, with the bulk of this expenditure attributable to inpatient or emergency department care. Furthermore, cirrhosis causes wide-ranging socioeconomic effects including reduced employment and loss of productivity, and impaired health-related quality of life (HRQoL). In this editorial, we provide an update on our 2021 review of emerging synthetic drugs for cirrhosis [2], consider the setbacks and progress over the past three years, and provide some future insights.<br/

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It therefore represents both a global public health threat and a precision medicine challenge. The use of artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in the context of analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national level ‘data commons’ (SteatoSITE) as an exemplar, the opportunities as well as the technical challenges of large-scale databases in MASLD research are highlighted

Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records

INTRODUCTION AND OBJECTIVES: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model.PATIENTS AND METHODS: n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n = 528 MASLD patients.RESULTS: In-sample model performance achieved AUROC curve 0.74-0.90 (95 % CI: 0.72-0.94), sensitivity 64 %-82 %, specificity 75 %-92 % and Positive Predictive Value (PPV) 94 %-98 %. Out-of-sample model validation had AUROC 0.70-0.86 (95 % CI: 0.67-0.90), sensitivity 69 %-70 %, specificity 96 %-97 % and PPV 75 %-77 %. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay &gt;14 days.CONCLUSIONS: A TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions.</p

Antifibrotic therapy in nonalcoholic steatohepatitis: time for a human-centric approach

Nonalcoholic steatohepatitis (NASH) might soon become the leading cause of end-stage liver disease and indication for liver transplantation worldwide. Fibrosis severity is the only histological predictor of liver-related morbidity and mortality in NASH identified to date. Moreover, fibrosis regression is associated with improved clinical outcomes. However, despite numerous clinical trials of plausible drug candidates, an approved antifibrotic therapy remains elusive. Increased understanding of NASH susceptibility and pathogenesis, emerging human multiomics profiling, integration of electronic health record data and modern pharmacology techniques hold enormous promise in delivering a paradigm shift in antifibrotic drug development in NASH. There is a strong rationale for drug combinations to boost efficacy, and precision medicine strategies targeting key genetic modifiers of NASH are emerging. In this Perspective, we discuss why antifibrotic effects observed in NASH pharmacotherapy trials have been underwhelming and outline potential approaches to improve the likelihood of future clinical success

Reliable computational quantification of liver fibrosis is compromised by inherent staining variation

Biopsy remains the gold standard measure for staging liver disease, both to inform prognosis and to assess the response to a given treatment. Semiquantitative scores such as the Ishak fibrosis score are used for evaluation. These scores are utilised in clinical trials, with the US Food and Drug Administration mandating particular scores as inclusion criteria for participants and using the change in score as evidence of treatment efficacy. There is an urgent need for improved, quantitative assessment of liver biopsies to detect small incremental changes in liver architecture over the course of a clinical trial. Artificial intelligence (AI) methods have been proposed as a way to increase the amount of information extracted from a biopsy and to potentially remove bias introduced by manual scoring. We have trained and evaluated an AI tool for measuring the amount of scarring in sections of picrosirius red-stained liver. The AI methodology was compared with both manual scoring and widely available colour space thresholding. Four sequential sections from each case were stained on two separate occasions by two independent clinical laboratories using routine protocols to study the effect of inter- and intra-laboratory staining variation on these tools. Finally, we compared these methods to second harmonic generation (SHG) imaging, a stain-free quantitative measure of collagen. Although AI methods provided a modest improvement over simpler computer-assisted measures, staining variation both within and between labs had a dramatic effect on quantitation, with manual assignment of scar proportion the most consistent. Manual assessment also correlated the most strongly with collagen measured by SHG. In conclusion, results suggest that computational measures of liver scarring from stained sections are compromised by inter- and intra-laboratory staining. Stain-free quantitative measurement using SHG avoids staining-related variation and may prove more accurate in detecting small changes in scarring that may occur in therapeutic trials

All coffee types decrease the risk of adverse clinical outcomes in chronic liver disease: A UK Biobank study

Abstract Background Chronic liver disease (CLD) is a growing cause of morbidity and mortality worldwide, particularly in low to middle-income countries with high disease burden and limited treatment availability. Coffee consumption has been linked with lower rates of CLD, but little is known about the effects of different coffee types, which vary in chemical composition. This study aimed to investigate associations of coffee consumption, including decaffeinated, instant and ground coffee, with chronic liver disease outcomes. Methods A total of 494,585 UK Biobank participants with known coffee consumption and electronic linkage to hospital, death and cancer records were included in this study. Cox regression was used to estimate hazard ratios (HR) of incident CLD, incident CLD or steatosis, incident hepatocellular carcinoma (HCC) and death from CLD according to coffee consumption of any type as well as for decaffeinated, instant and ground coffee individually. Results Among 384,818 coffee drinkers and 109,767 non-coffee drinkers, there were 3600 cases of CLD, 5439 cases of CLD or steatosis, 184 cases of HCC and 301 deaths from CLD during a median follow-up of 10.7 years. Compared to non-coffee drinkers, coffee drinkers had lower adjusted HRs of CLD (HR 0.79, 95% CI 0.72–0.86), CLD or steatosis (HR 0.80, 95% CI 0.75–0.86), death from CLD (HR 0.51, 95% CI 0.39–0.67) and HCC (HR 0.80, 95% CI 0.54–1.19). The associations for decaffeinated, instant and ground coffee individually were similar to all types combined. Conclusion The finding that all types of coffee are protective against CLD is significant given the increasing incidence of CLD worldwide and the potential of coffee as an intervention to prevent CLD onset or progression

The eye as a non-invasive window to the microcirculation in liver cirrhosis: a prospective pilot study

Microcirculatory dysfunction is associated with organ failure, poor response to vasoactive drugs and increased mortality in cirrhosis, but monitoring techniques are not established. We hypothesized that the chorioretinal structures of the eye could be visualized as a non-invasive proxy of the systemic microvasculature in cirrhosis and would correlate with renal dysfunction. Optical Coherence Tomography (OCT) was performed to image the retina in n = 55 cirrhosis patients being assessed for liver transplantation. OCT parameters were compared with established cohorts of age- and sex-matched healthy volunteers (HV) and patients with chronic kidney disease (CKD). Retinal thickness, macular volume and choroidal thickness were significantly reduced relative to HV and comparable to CKD patients (macular volume: HV vs. cirrhosis mean difference 0.44 mm3 (95% CI 0.26&ndash;0.61), p &le; 0.0001). Reduced retinal thickness and macular volume correlated with renal dysfunction in cirrhosis (macular volume vs. MDRD-6 eGFR r = 0.40, p = 0.006). Retinal changes had resolved substantially 6 weeks following transplantation. There was an inverse association between choroidal thickness and circulating markers of endothelial dysfunction (endothelin-1 r = &minus;0.49, p &le; 0.001; von Willebrand factor r = &minus;0.32, p &le; 0.05). Retinal OCT may represent a non-invasive window to the microcirculation in cirrhosis and a dynamic measure of renal and endothelial dysfunction. Validation in different cirrhosis populations is now required

Assessment of Haemodynamic Response to Nonselective Beta-Blockers in Portal Hypertension by Phase-Contrast Magnetic Resonance Angiography

A significant unmet need exists for accurate, reproducible, noninvasive diagnostic tools to assess and monitor portal hypertension (PHT). We report the first use of quantitative MRI markers for the haemodynamic assessment of nonselective beta-blockers (NSBB) in PHT. In a randomized parallel feasibility study in 22 adult patients with PHT and a clinical indication for NSBB, we acquired haemodynamic data at baseline and after 4 weeks of NSBB (propranolol or carvedilol) using phase-contrast MR angiography (PC-MRA) in selected intra-abdominal vessels. T1 mapping of liver and spleen was undertaken to assess changes in tissue composition. Target NSBB dose was achieved in 82%. There was a substantial reduction from baseline in mean average flow in the superior abdominal aorta after 4 weeks of NSBB therapy (4.49±0.98 versus 3.82±0.86 L/min, P=0.03) but there were no statistically significant differences in flow in any other vessels, even in patients with >25% decrease in heart rate (47% of patients). Mean percentage change in liver and spleen T1 following NSBB was small and highly variable. In conclusion, PC-MRA was able to detect reduction in cardiac output by NSBB but did not detect significant changes in visceral blood flow or T1. This trial was registered with the ISRCTN registry (ISRCTN98001632)