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PNPLA3 I148M and Hepatocellular Carcinoma
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-03-03 DOI: 10.1111/liv.70051
Federica Tavaglione, Grazia Pennisi, Serena Pelusi

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. With the widespread implementation of HBV vaccination and the availability of highly effective antiviral therapies, the incidence of metabolic dysfunction-associated steatotic liver disease (MASLD)-related HCC has proportionally increased. Notably, up to 20%–30% of MASLD–related HCC cases develop in the absence of overt cirrhosis. Several genetic variants, primarily in genes related to lipid metabolism, play a key role in HCC development in individuals with MASLD and alcohol-related liver disease. Among these, the rs738409 C>G polymorphism (I148M) in the patatin-like phospholipase domain-containing protein 3 (PNPLA3) gene is the strongest genetic factor predisposing to the entire spectrum of MASLD conditions, including cirrhosis and HCC. Importantly, combining PNPLA3 I148M with multiple genetic variants robustly associated with progressive liver disease (i.e., polygenic risk scores) improves risk stratification and prediction of HCC in at-risk individuals compared to the single variant alone. In this review, we will discuss the latest evidence on the epidemiology of HCC and the contribution of PNPLA3 and PNPLA3-based polygenic risk scores to the development of HCC in at-risk individuals.

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引用次数: 0
Steatotic Liver Disease in Younger Adults is Associated With Altered Gut Microbiology
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-25 DOI: 10.1111/liv.70032
Yasmina Tashkent, Jocelyn M. Choo, Alyson Richard, Zhengyi Wang, Luis Calzadilla-Bertot, Egi Vasil, Sophie Miller, Steven L. Taylor, Kerry L. Ivey, Richard Woodman, Brendan Adler, Oyekoya T. Ayonrinde, John K. Olynyk, Lawrence J. Beilin, Trevor A. Mori, Alan J. Wigg, Kate R. Muller, Leon A. Adams, Geraint B. Rogers

Background and Aims

Steatotic liver disease (SLD) is a leading cause of chronic liver disease worldwide. As SLD pathogenesis has been linked to gut microbiome alterations, we aimed to identify SLD-associated gut microbiome features early in SLD development by utilising a highly characterised cohort of community-dwelling younger adults.

Methods and Results

At age 27 years, 588 participants of the Raine Study Generation 2 underwent cross-sectional assessment. Hepatic steatosis was quantified using a validated magnetic resonance imaging (MRI) volumetric liver fat fraction (VLFF) equation (HepaFat). Of the 588 participants, 488 (83%) were classified as having ‘no SLD’ (VLFF ≤ 3.55%), 76 (12.9%) with ‘mild–moderate’ SLD (VLFF: 3.56%–13.4%) and 24 (4.10%) with ‘severe’ SLD (VLFF > 13.4%). Stool microbiome profiling identified an association between severe SLD and lower microbiota alpha diversity (observed features [p = 0.015], Pielou evenness [p = 0.001] and Shannon diversity [p = 0.002]) compared to no SLD. Faecal microbiota composition differed significantly between no SLD and both mild–moderate (p = 0.004) and severe SLD groups (p = 0.001). There was no significant difference in microbiota dispersion between SLD groups. Reduced relative abundance of short-chain fatty acid producing bacteria, and higher levels of proinflammatory bacterial taxa, were both significantly associated with severe SLD (q < 0.05).

Conclusions

SLD in younger adults is associated with reduced intestinal microbial diversity and a pattern of bacterial taxa depletion that is consistent with other chronic inflammatory conditions. Our characterisation of gut microbiome characteristics in early SLD development provides a potential basis for risk identification and reduction.

Trial Registration

The Raine Study is registered in the Australian New Zealand Clinical Trials Registry (ACTRN12617001599369)

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引用次数: 0
The Association Between Metabolic Dysfunction-Associated Steatotic Liver Disease and Change in Liver Stiffness in Patients With Chronic Hepatitis B
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-25 DOI: 10.1111/liv.70042
Lesley A. Patmore, Kirsi van Eekhout, Özgür M. Koc, Robert J. de Knegt, Harry L. A. Janssen, Willem P. Brouwer, Matthijs Kramer, Pieter Honkoop, Joep de Bruijne, Greet J. Boland, Douwe F. Postma, Hans Blokzijl, Robert A. de Man, R. Bart Takkenberg, Milan J. Sonneveld

Background and Aims

Metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with an increased risk of liver-related events in patients with chronic hepatitis B (CHB), possibly by accelerating fibrosis progression. Therefore, we studied the influence of MASLD on liver stiffness measurement (LSM) kinetics in CHB patients.

Methods

We conducted a multicenter retrospective cohort study of CHB patients with at least two LSM with FibroScan. We studied the absolute change in LSM and the change in LSM stage from the first LSM to the most recent LSM among CHB patients with MASLD compared to patients without MASLD.

Results

We analysed 1055 CHB patients; 259 (28.0%) had MASLD. Patients with MASLD had a higher first and last LSM (6.1 vs. 5.2 kPa and 5.6 vs. 4.7 kPa, p < 0.001), were significantly less likely to achieve a decrease in LSM stage (52.8% vs. 74% p < 0.001) and were more likely to experience an increase in LSM stage (19.3% vs. 13.6%, p = 0.035) during follow-up. 417 (39.5%) patients initiated antiviral therapy (AVT) which was associated with a decline in LSM (p < 0.001). However, patients with MASLD who were treated were less likely to decrease in LSM stage (52.4% vs. 77.0%, p < 0.001) and were more likely to experience an increase in LSM stage (23.5% vs. 12.8%, p = 0.021) despite AVT.

Conclusion

Presence of MASLD was independently associated with higher LSM in untreated CHB patients and with less decline in LSM after initiation of AVT. Furthermore, CHB patients with MASLD were more likely to experience an increase in LSM despite AVT.

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引用次数: 0
The Prevalence of Acute and Decompensated Cirrhotic Autoimmune Hepatitis Is Increasing
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-24 DOI: 10.1111/liv.16116
Yunke Peng, Yujie Zhang, Li Yang, Xiaoli Fan
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引用次数: 0
MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-24 DOI: 10.1111/liv.16205
Tianying Zheng, Yajing Zhu, Hanyu Jiang, Chongtu Yang, Yuxiang Ye, Mustafa R. Bashir, Chenhui Li, Liling Long, Shishi Luo, Bin Song, Yinan Chen, Yidi Chen

Background & Aims

Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally validate an MRI-based topology DL model for preoperative prediction of MVI.

Methods

This dual-centre retrospective study included consecutive surgically treated HCC patients from two tertiary care hospitals. Automatic liver and tumour segmentations were performed with DL methods. A pure convolutional neural network (CNN) model, a topology-CNN (TopoCNN) model and a topology-CNN-clinical (TopoCNN+Clinic) model were developed and externally validated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Cox regression analyses were conducted to identify risk factors for recurrence-free survival within 2 years (early RFS) and overall survival (OS).

Results

In total, 589 patients were included (292 [49.6%] with pathologically confirmed MVI). The AUCs of the TopoCNN and TopoCNN+Clinic models were 0.890 and 0.895 for the internal test dataset and 0.871 and 0.879 for the external test dataset, respectively. For tumours ≤ 3.0 cm, the AUCs of the TopoCNN and TopoCNN+Clinic models were 0.879 and 0.929 for the internal test dataset, and 0.763 and 0.758 for the external test dataset. The TopoCNN-derived MVI prediction probability was an independent risk factor for early RFS (hazard ratio 6.64) and OS (hazard ratio 13.33).

Conclusions

The MRI topological DL model based on automatic liver and tumour segmentation could accurately predict MVI and effectively stratify postoperative early RFS and OS, which may assist in personalised treatment decision-making.

{"title":"MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC","authors":"Tianying Zheng,&nbsp;Yajing Zhu,&nbsp;Hanyu Jiang,&nbsp;Chongtu Yang,&nbsp;Yuxiang Ye,&nbsp;Mustafa R. Bashir,&nbsp;Chenhui Li,&nbsp;Liling Long,&nbsp;Shishi Luo,&nbsp;Bin Song,&nbsp;Yinan Chen,&nbsp;Yidi Chen","doi":"10.1111/liv.16205","DOIUrl":"https://doi.org/10.1111/liv.16205","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background &amp; Aims</h3>\u0000 \u0000 <p>Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally validate an MRI-based topology DL model for preoperative prediction of MVI.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This dual-centre retrospective study included consecutive surgically treated HCC patients from two tertiary care hospitals. Automatic liver and tumour segmentations were performed with DL methods. A pure convolutional neural network (CNN) model, a topology-CNN (TopoCNN) model and a topology-CNN-clinical (TopoCNN+Clinic) model were developed and externally validated. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Cox regression analyses were conducted to identify risk factors for recurrence-free survival within 2 years (early RFS) and overall survival (OS).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In total, 589 patients were included (292 [49.6%] with pathologically confirmed MVI). The AUCs of the TopoCNN and TopoCNN+Clinic models were 0.890 and 0.895 for the internal test dataset and 0.871 and 0.879 for the external test dataset, respectively. For tumours ≤ 3.0 cm, the AUCs of the TopoCNN and TopoCNN+Clinic models were 0.879 and 0.929 for the internal test dataset, and 0.763 and 0.758 for the external test dataset. The TopoCNN-derived MVI prediction probability was an independent risk factor for early RFS (hazard ratio 6.64) and OS (hazard ratio 13.33).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The MRI topological DL model based on automatic liver and tumour segmentation could accurately predict MVI and effectively stratify postoperative early RFS and OS, which may assist in personalised treatment decision-making.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18101,"journal":{"name":"Liver International","volume":"45 3","pages":""},"PeriodicalIF":6.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/liv.16205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143475383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
AI-Driven MRI for Microvascular Invasion Prediction in HCC: A Reply to the Interpretable and Open AI Models HCC
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-24 DOI: 10.1111/liv.70013
Yidi Chen, Tianying Zheng, Yinan Chen, Bin Song
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引用次数: 0
Rencofilstat Treatment Improves Liver Function in MASH With Advanced Fibrosis as Quantified by HepQuant DuO
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-21 DOI: 10.1111/liv.70036
Stephen A. Harrison, Patrick Mayo, Todd Hobbs, Caroline Zhao, Carlos Canizares, Robert Foster, Michael P. McRae, Steve M. Helmke, Gregory T. Everson

Background and Aims

Rencofilstat inhibits cyclophilin to reduce hepatic inflammation and fibrosis, which, in turn, could improve liver function and reduce portal-systemic shunting. Since HepQuant quantifies liver function and portal-systemic shunting, it was used to measure the hepatic effects of rencofilstat treatment of MASH with advanced fibrosis.

Methods

Seventy subjects with suspected ≥ F3 MASH, defined from liver biopsy or AGILE 3+ ≥ 0.53, were randomised to rencofilstat 75 mg/d (n = 24), 150 mg/d (n = 23) or 225 mg/d (n = 23), and tested by HepQuant at baseline, 60 and 120 days. The DuO version included oral dosing of d4-cholate and two blood samples (20 and 60 min). DuO's disease severity index (DSI) and portal-systemic shunting fraction (SHUNT%) were evaluated for changes from baseline at 60 and 120 days of rencofilstat treatment.

Results

Across all subjects, there was a significant decrease in SHUNT% both at Day 60 (−1.67%, p = 0.0156) and Day 120 (−1.55%, p = 0.0441). In the 225 mg rencofilstat arm, 56% of subjects (10/18) were responders by Day 120 (p = 0.0549), and their DSIs improved with a mean change of −1.61 (p = 0.0190). Across all treatment arms, subjects with DSI > 18.3 at baseline had the greatest improvement with treatment (ΔDSI = −2.59, p = 0.0053).

Conclusion

Although further studies are warranted, the decreases in DSI and SHUNT% suggest that rencofilstat 225 mg/d improves hepatic function and portal-systemic shunting. HepQuant DuO is simple to administer, well-tolerated and a useful tool for detecting the hepatic effects of treatment.

Trial Registration

The study was registered at ClinicalTrials.gov, NCT05461105

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引用次数: 0
A Commentary on ‘Safety of Anticoagulation When Undergoing Endoscopic Variceal Ligation: A Systematic Review and Meta-Analysis’
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-21 DOI: 10.1111/liv.70021
Zhenwei Wang, Ying Zhu, Liangliang Chen
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引用次数: 0
Artificial Intelligence: An Emerging Tool for Studying Drug-Induced Liver Injury 人工智能:研究药物性肝损伤的新兴工具
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-21 DOI: 10.1111/liv.70038
Hao Niu, Ismael Alvarez-Alvarez, Minjun Chen

Drug-induced liver injury (DILI) is a complex and potentially severe adverse reaction to drugs, herbal products or dietary supplements. DILI can mimic other liver diseases clinical presentation, and currently lacks specific diagnostic biomarkers, which hinders its diagnosis. In some cases, DILI may progress to acute liver failure. Given its public health risk, novel methodologies to enhance the understanding of DILI are crucial. Recently, the increasing availability of larger datasets has highlighted artificial intelligence (AI) as a powerful tool to construct complex models. In this review, we summarise the evidence about the use of AI in DILI research, explaining fundamental AI concepts and its subfields. We present findings from AI-based approaches in DILI investigations for risk stratification, prognostic evaluation and causality assessment and discuss the adoption of natural language processing (NLP) and large language models (LLM) in the clinical setting. Finally, we explore future perspectives and challenges in utilising AI for DILI research.

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引用次数: 0
Author's Reply: Commentary on Safety of Anticoagulation When Undergoing Endoscopic Variceal Ligation
IF 6 2区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Pub Date : 2025-02-21 DOI: 10.1111/liv.70034
Jing Hong Loo, Joo Wei Ethan Quek, Yu Jun Wong
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引用次数: 0
期刊
Liver International
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