Nikhil Kirtipal, Youngchang Seo, Jangwon Son, Sunjae Lee
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Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome's function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.</p>","PeriodicalId":11153,"journal":{"name":"Diabetes & Metabolism Journal","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449821/pdf/","citationCount":"0","resultStr":"{\"title\":\"Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response.\",\"authors\":\"Nikhil Kirtipal, Youngchang Seo, Jangwon Son, Sunjae Lee\",\"doi\":\"10.4093/dmj.2024.0382\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body's response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome's role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome's function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.</p>\",\"PeriodicalId\":11153,\"journal\":{\"name\":\"Diabetes & Metabolism Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449821/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diabetes & Metabolism Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.4093/dmj.2024.0382\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/12 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes & Metabolism Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.4093/dmj.2024.0382","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/12 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response.
The human gut microbiota is increasingly recognized as a pivotal factor in diabetes management, playing a significant role in the body's response to treatment. However, it is important to understand that long-term usage of medicines like metformin and other diabetic treatments can result in problems, gastrointestinal discomfort, and dysbiosis of the gut flora. Advanced sequencing technologies have improved our understanding of the gut microbiome's role in diabetes, uncovering complex interactions between microbial composition and metabolic health. We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. This study holds promise for specialized diabetic therapy. Despite significant advances, some concerns remain unanswered, including understanding the complex relationship between diabetes etiology and gut microbiota, as well as developing user-friendly technological innovations. This mini-review explores the relationship between multiomics, precision medicine, and machine learning to improve our understanding of the gut microbiome's function in diabetes. In the era of precision medicine, the ultimate goal is to improve patient outcomes through personalized treatments.
期刊介绍:
The aims of the Diabetes & Metabolism Journal are to contribute to the cure of and education about diabetes mellitus, and the advancement of diabetology through the sharing of scientific information on the latest developments in diabetology among members of the Korean Diabetes Association and other international societies.
The Journal publishes articles on basic and clinical studies, focusing on areas such as metabolism, epidemiology, pathogenesis, complications, and treatments relevant to diabetes mellitus. It also publishes articles covering obesity and cardiovascular disease. Articles on translational research and timely issues including ubiquitous care or new technology in the management of diabetes and metabolic disorders are welcome. In addition, genome research, meta-analysis, and randomized controlled studies are welcome for publication.
The editorial board invites articles from international research or clinical study groups. Publication is determined by the editors and peer reviewers, who are experts in their specific fields of diabetology.