Pub Date : 2025-09-03DOI: 10.1038/s41575-025-01106-3
Chaoran Yang, Matthew Snelson, Assam El-Osta, Francine Z. Marques
The effects of diet and nutrition extend beyond individual health: food intake before conception or during pregnancy and lactation can affect the health of offspring. Diet is one of the most powerful modulators of the gut microbiome, influencing gene–environment interactions, with several emerging mechanisms pointing to the microbiome–metabolite–epigenome axis. In this Review, we discuss the effect of dietary changes on the gametes (‘gut–germline axis’) or in utero (‘gut–neonatal axis’) that may change the predisposition of offspring to several non-communicable diseases. Examples of diets discussed are those that detrimentally modulate the parental microbiota and lead to epigenetic changes in the progeny, including Western diets characterized by high saturated fat and low protein or fibre intake. We summarize studies using animal models, which suggest that these diets can have long-lasting effects on the offspring microbiome, epigenome and phenotype, particularly across the cardiometabolic and immune systems, and discuss the limitations of current studies as well as future directions for the field. Translational research investigating the benefits of parental dietary interventions before and during pregnancy, mainly using personalized approaches, is needed. This would, in turn, reduce rates of non-communicable diseases in generations to come. In this Review, Marques and colleagues discuss the evidence regarding the effects of parental diet on the health of offspring, with a focus on how changes to the gut microbiome alter epigenomic responses in the offspring.
{"title":"Parental diet and offspring health: a role for the gut microbiome via epigenetics","authors":"Chaoran Yang, Matthew Snelson, Assam El-Osta, Francine Z. Marques","doi":"10.1038/s41575-025-01106-3","DOIUrl":"10.1038/s41575-025-01106-3","url":null,"abstract":"The effects of diet and nutrition extend beyond individual health: food intake before conception or during pregnancy and lactation can affect the health of offspring. Diet is one of the most powerful modulators of the gut microbiome, influencing gene–environment interactions, with several emerging mechanisms pointing to the microbiome–metabolite–epigenome axis. In this Review, we discuss the effect of dietary changes on the gametes (‘gut–germline axis’) or in utero (‘gut–neonatal axis’) that may change the predisposition of offspring to several non-communicable diseases. Examples of diets discussed are those that detrimentally modulate the parental microbiota and lead to epigenetic changes in the progeny, including Western diets characterized by high saturated fat and low protein or fibre intake. We summarize studies using animal models, which suggest that these diets can have long-lasting effects on the offspring microbiome, epigenome and phenotype, particularly across the cardiometabolic and immune systems, and discuss the limitations of current studies as well as future directions for the field. Translational research investigating the benefits of parental dietary interventions before and during pregnancy, mainly using personalized approaches, is needed. This would, in turn, reduce rates of non-communicable diseases in generations to come. In this Review, Marques and colleagues discuss the evidence regarding the effects of parental diet on the health of offspring, with a focus on how changes to the gut microbiome alter epigenomic responses in the offspring.","PeriodicalId":18793,"journal":{"name":"Nature Reviews Gastroenterology &Hepatology","volume":"22 11","pages":"755-772"},"PeriodicalIF":51.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144960109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-03DOI: 10.1038/s41575-025-01111-6
Florian Huwyler, Jonas Binz, Leslie Cunningham, Matthias Pfister, Martin J. Schuler, Mark W. Tibbitt, Pierre-Alain Clavien
Machine perfusion is an emerging and transformative technology for dynamic organ preservation, assessment and repair. Whereas allografts continuously degrade during static cold storage, short-term perfusion can preserve high-quality organs for hours, enabling assessment, regional transport and improved logistics. Long-term perfusion for multiple days might extend the potential of clinical machine perfusion in the future, allowing for the assessment, reconditioning and repair of marginal or injured grafts for which more time is needed. In addition, it might convert transplantation, which is now semi-elective thanks to short-term perfusion, to a fully elective procedure via customized machines and associated protocols that maintain organs ex situ for up to 2 weeks. The advent of long-term organ perfusion provides tremendous potential to improve organ evaluation and selection, to recondition or repair marginal grafts and, ultimately, to expand the pool of grafts available for transplantation. In this Perspective, we discuss design considerations, guidelines for use, and future perspectives of machine perfusion in the context of organ assessment and repair, with a focus on the liver. In this Perspective article, Huwyler, Binz and colleagues discuss the future of long-term normothermic machine perfusion for livers and propose a staged assessment approach for ex situ perfused organs.
{"title":"Beyond preservation: future machine perfusion for liver assessment and repair","authors":"Florian Huwyler, Jonas Binz, Leslie Cunningham, Matthias Pfister, Martin J. Schuler, Mark W. Tibbitt, Pierre-Alain Clavien","doi":"10.1038/s41575-025-01111-6","DOIUrl":"10.1038/s41575-025-01111-6","url":null,"abstract":"Machine perfusion is an emerging and transformative technology for dynamic organ preservation, assessment and repair. Whereas allografts continuously degrade during static cold storage, short-term perfusion can preserve high-quality organs for hours, enabling assessment, regional transport and improved logistics. Long-term perfusion for multiple days might extend the potential of clinical machine perfusion in the future, allowing for the assessment, reconditioning and repair of marginal or injured grafts for which more time is needed. In addition, it might convert transplantation, which is now semi-elective thanks to short-term perfusion, to a fully elective procedure via customized machines and associated protocols that maintain organs ex situ for up to 2 weeks. The advent of long-term organ perfusion provides tremendous potential to improve organ evaluation and selection, to recondition or repair marginal grafts and, ultimately, to expand the pool of grafts available for transplantation. In this Perspective, we discuss design considerations, guidelines for use, and future perspectives of machine perfusion in the context of organ assessment and repair, with a focus on the liver. In this Perspective article, Huwyler, Binz and colleagues discuss the future of long-term normothermic machine perfusion for livers and propose a staged assessment approach for ex situ perfused organs.","PeriodicalId":18793,"journal":{"name":"Nature Reviews Gastroenterology &Hepatology","volume":"22 10","pages":"721-733"},"PeriodicalIF":51.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144930667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-27DOI: 10.1038/s41575-025-01102-7
Anoohya N. Muppirala, Mitchell T. Ringuet, Alain J. Benitez, Kristen M. Smith-Edwards, Keith A. Sharkey, Nathalie Vergnolle
The Little Brain Big Brain meeting was established more than 30 years ago as an opportunity for early career researchers to meet, present and discuss exciting new developments in the field of enteric neuroscience and neurogastroenterology. Crucially, the meeting is organized by young investigators, for young investigators. In this Viewpoint, past attendees and organizers of the Little Brain Big Brain meeting discuss their research interests, share their experience with this unique meeting and provide insights into progress in the field of enteric neuroscience and neurogastroenterology and its future outlook. In this Viewpoint, past attendees and organizers of the Little Brain Big Brain share their experience with this unique meeting and their insights into the field of enteric neuroscience and neurogastroenterology.
Little Brain Big Brain会议成立于30多年前，是早期职业研究人员会面、展示和讨论肠道神经科学和神经胃肠病学领域令人兴奋的新发展的机会。至关重要的是，这次会议是由年轻的研究者组织的，为年轻的研究者服务。在这篇文章中，Little Brain Big Brain会议的过去的与会者和组织者讨论了他们的研究兴趣，分享了他们在这个独特的会议上的经验，并提供了肠道神经科学和神经胃肠病学领域的进展及其未来展望。
{"title":"Next-generation enteric neuroscience — fostering the future of the field","authors":"Anoohya N. Muppirala, Mitchell T. Ringuet, Alain J. Benitez, Kristen M. Smith-Edwards, Keith A. Sharkey, Nathalie Vergnolle","doi":"10.1038/s41575-025-01102-7","DOIUrl":"10.1038/s41575-025-01102-7","url":null,"abstract":"The Little Brain Big Brain meeting was established more than 30 years ago as an opportunity for early career researchers to meet, present and discuss exciting new developments in the field of enteric neuroscience and neurogastroenterology. Crucially, the meeting is organized by young investigators, for young investigators. In this Viewpoint, past attendees and organizers of the Little Brain Big Brain meeting discuss their research interests, share their experience with this unique meeting and provide insights into progress in the field of enteric neuroscience and neurogastroenterology and its future outlook. In this Viewpoint, past attendees and organizers of the Little Brain Big Brain share their experience with this unique meeting and their insights into the field of enteric neuroscience and neurogastroenterology.","PeriodicalId":18793,"journal":{"name":"Nature Reviews Gastroenterology &Hepatology","volume":"22 10","pages":"673-679"},"PeriodicalIF":51.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144905820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-22DOI: 10.1038/s41575-025-01108-1
Isabella Catharina Wiest, Mamatha Bhat, Jan Clusmann, Carolin V. Schneider, Xiaofeng Jiang, Jakob Nikolas Kather
Clinical decision making in gastroenterology and hepatology has become increasingly complex and challenging for physicians. This growing complexity can be addressed by computational tools that support clinical decisions. Although numerous clinical decision support systems (CDSS) have emerged, they have faced difficulties with real-world performance and generalizability, resulting in limited clinical adoption. Generative artificial intelligence (AI), particularly large language models (LLMs), are introducing new possibilities for CDSS by offering more flexible and adaptable support that better reflects complex clinical scenarios. LLMs can process unstructured text, including patient data and medical guidelines, and integrate various information sources with high accuracy, especially when augmented with retrieval-augmented generation. Thus, LLMs can provide dynamic, context-specific support by generating personalized treatment recommendations, identifying potential complications based on patient history, and enabling natural language interactions with health-care providers. However, important challenges persist, particularly regarding biases, hallucinations, interoperability barriers, and proper training of health-care providers. We examine the parallel evolution of the complexity in clinical management in gastroenterology and hepatology, and the technical developments leading to current generative AI models. We discuss how these advances are converging to create effective CDSS, providing a conceptual basis for further development and clinical adoption of these systems. This Perspective discusses the use and potential of large language models and clinical decision support systems in gastroenterology and hepatology, highlighting opportunities, challenges and limitations of large language models and clinical decision support systems in clinical practice. Key directions for research and insights into clinical integration and safe use are also discussed.
{"title":"Large language models for clinical decision support in gastroenterology and hepatology","authors":"Isabella Catharina Wiest, Mamatha Bhat, Jan Clusmann, Carolin V. Schneider, Xiaofeng Jiang, Jakob Nikolas Kather","doi":"10.1038/s41575-025-01108-1","DOIUrl":"10.1038/s41575-025-01108-1","url":null,"abstract":"Clinical decision making in gastroenterology and hepatology has become increasingly complex and challenging for physicians. This growing complexity can be addressed by computational tools that support clinical decisions. Although numerous clinical decision support systems (CDSS) have emerged, they have faced difficulties with real-world performance and generalizability, resulting in limited clinical adoption. Generative artificial intelligence (AI), particularly large language models (LLMs), are introducing new possibilities for CDSS by offering more flexible and adaptable support that better reflects complex clinical scenarios. LLMs can process unstructured text, including patient data and medical guidelines, and integrate various information sources with high accuracy, especially when augmented with retrieval-augmented generation. Thus, LLMs can provide dynamic, context-specific support by generating personalized treatment recommendations, identifying potential complications based on patient history, and enabling natural language interactions with health-care providers. However, important challenges persist, particularly regarding biases, hallucinations, interoperability barriers, and proper training of health-care providers. We examine the parallel evolution of the complexity in clinical management in gastroenterology and hepatology, and the technical developments leading to current generative AI models. We discuss how these advances are converging to create effective CDSS, providing a conceptual basis for further development and clinical adoption of these systems. This Perspective discusses the use and potential of large language models and clinical decision support systems in gastroenterology and hepatology, highlighting opportunities, challenges and limitations of large language models and clinical decision support systems in clinical practice. Key directions for research and insights into clinical integration and safe use are also discussed.","PeriodicalId":18793,"journal":{"name":"Nature Reviews Gastroenterology &Hepatology","volume":"22 11","pages":"773-787"},"PeriodicalIF":51.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144899503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-07DOI: 10.1038/s41575-025-01103-6
Julien Calderaro
Pathology is a fast-changing discipline, owing to developments in high-throughput molecular technologies and artificial intelligence. In this Comment, I discuss how these advances will shape the future of gastrointestinal and liver pathology.
{"title":"The future of pathology in gastroenterology and hepatology","authors":"Julien Calderaro","doi":"10.1038/s41575-025-01103-6","DOIUrl":"10.1038/s41575-025-01103-6","url":null,"abstract":"Pathology is a fast-changing discipline, owing to developments in high-throughput molecular technologies and artificial intelligence. In this Comment, I discuss how these advances will shape the future of gastrointestinal and liver pathology.","PeriodicalId":18793,"journal":{"name":"Nature Reviews Gastroenterology &Hepatology","volume":"22 9","pages":"598-599"},"PeriodicalIF":51.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-31DOI: 10.1038/s41575-025-01100-9
Alberto Caminero, Carolina Tropini, Mireia Valles-Colomer, Dennis L. Shung, Sean M. Gibbons, Michael G. Surette, Harry Sokol, Nicholas J. Tomeo, Scientific Advisory Board of the Center for Gut Microbiome Research and Education of the American Gastroenterological Association, Phillip I. Tarr, Elena F. Verdu
The microbiome has critical roles in human health and disease. Advances in high-throughput sequencing and metabolomics have revolutionized our understanding of human gut microbial communities and identified plausible associations with a variety of disorders. However, microbiome research remains constrained by challenges in establishing causality, an over-reliance on correlative studies, and methodological and analytical limitations. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges; however, the seamless integration of preclinical models and clinical trials is crucial to maximizing the translational impact of microbiome studies. This manuscript critically evaluates best methodological practices and limitations in the field, focusing on how emerging AI tools can bridge the gap between microbial insights and clinical applications. Specifically, we emphasize the necessity of rigorous, reproducible methodologies that integrate multiomics approaches, preclinical models and clinical trials in the AI-driven era. We propose a practical framework for applying AI to microbiome studies, alongside strategic recommendations for clinical trial design, regulatory pathways, and best practices for microbiome-based informed diagnostics, AI training and clinical interventions. By establishing these guidelines, we aim to accelerate the translation of microbiome research into clinical practice, enabling precision medicine approaches informed by the human microbiome. Artificial intelligence (AI) is a powerful tool that could be applied to microbiome research. This Perspective discusses best practices and current limitations with the application of AI in microbiome data research, giving insights into future use and practical advice and recommendations on its use.
{"title":"Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI","authors":"Alberto Caminero, Carolina Tropini, Mireia Valles-Colomer, Dennis L. Shung, Sean M. Gibbons, Michael G. Surette, Harry Sokol, Nicholas J. Tomeo, Scientific Advisory Board of the Center for Gut Microbiome Research and Education of the American Gastroenterological Association, Phillip I. Tarr, Elena F. Verdu","doi":"10.1038/s41575-025-01100-9","DOIUrl":"10.1038/s41575-025-01100-9","url":null,"abstract":"The microbiome has critical roles in human health and disease. Advances in high-throughput sequencing and metabolomics have revolutionized our understanding of human gut microbial communities and identified plausible associations with a variety of disorders. However, microbiome research remains constrained by challenges in establishing causality, an over-reliance on correlative studies, and methodological and analytical limitations. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges; however, the seamless integration of preclinical models and clinical trials is crucial to maximizing the translational impact of microbiome studies. This manuscript critically evaluates best methodological practices and limitations in the field, focusing on how emerging AI tools can bridge the gap between microbial insights and clinical applications. Specifically, we emphasize the necessity of rigorous, reproducible methodologies that integrate multiomics approaches, preclinical models and clinical trials in the AI-driven era. We propose a practical framework for applying AI to microbiome studies, alongside strategic recommendations for clinical trial design, regulatory pathways, and best practices for microbiome-based informed diagnostics, AI training and clinical interventions. By establishing these guidelines, we aim to accelerate the translation of microbiome research into clinical practice, enabling precision medicine approaches informed by the human microbiome. Artificial intelligence (AI) is a powerful tool that could be applied to microbiome research. This Perspective discusses best practices and current limitations with the application of AI in microbiome data research, giving insights into future use and practical advice and recommendations on its use.","PeriodicalId":18793,"journal":{"name":"Nature Reviews Gastroenterology &Hepatology","volume":"22 11","pages":"788-803"},"PeriodicalIF":51.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-30DOI: 10.1038/s41575-025-01110-7
Eleni Kotsiliti
{"title":"Screening for hepatitis C in emergency departments","authors":"Eleni Kotsiliti","doi":"10.1038/s41575-025-01110-7","DOIUrl":"10.1038/s41575-025-01110-7","url":null,"abstract":"","PeriodicalId":18793,"journal":{"name":"Nature Reviews Gastroenterology &Hepatology","volume":"22 9","pages":"602-602"},"PeriodicalIF":51.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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