Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response.

IF 6.8 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Diabetes & Metabolism Journal Pub Date : 2024-09-01 Epub Date: 2024-09-12 DOI:10.4093/dmj.2024.0382
Nikhil Kirtipal, Youngchang Seo, Jangwon Son, Sunjae Lee
{"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":"48 5","pages":"821-836"},"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}
引用次数: 0

Abstract

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
预测个人血糖反应的人体微生物组系统生物学。
人们越来越认识到,人体肠道微生物群是糖尿病治疗的关键因素,在机体对治疗的反应中发挥着重要作用。然而,重要的是要知道,长期服用二甲双胍等药物和其他糖尿病治疗方法会导致问题、肠胃不适和肠道菌群失调。先进的测序技术提高了我们对肠道微生物组在糖尿病中作用的认识,揭示了微生物组成与代谢健康之间复杂的相互作用。我们通过研究各种组学数据,包括基因组学、转录组学、表观基因组学、蛋白质组学、代谢组学和元基因组学,探索肠道微生物群如何影响葡萄糖代谢和胰岛素敏感性。目前正在应用机器学习算法和基因组规模建模来寻找与糖尿病风险相关的微生物生物标志物、预测疾病进展并指导定制疗法。这项研究为专门的糖尿病治疗带来了希望。尽管取得了重大进展,但一些问题仍未得到解决,包括了解糖尿病病因与肠道微生物群之间的复杂关系,以及开发用户友好型技术创新。这篇微型综述探讨了多组学、精准医疗和机器学习之间的关系,以增进我们对肠道微生物组在糖尿病中的功能的了解。在精准医疗时代,最终目标是通过个性化治疗改善患者的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Diabetes & Metabolism Journal
Diabetes & Metabolism Journal Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
10.40
自引率
6.80%
发文量
92
审稿时长
52 weeks
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.
期刊最新文献
Do Time-Dependent Repeated Measures of Anthropometric and Body Composition Indices Improve the Prediction of Incident Diabetes in the Cohort Study? Findings from a Community-Based Korean Genome and Epidemiology Study. Efficacy and Safety of Automated Insulin Delivery Systems in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis. Exon Sequencing of HNF1β in Chinese Patients with Early-Onset Diabetes. Impact of Meal Frequency on Insulin Resistance in Middle-Aged and Older Adults: A Prospective Cohort Study. Rbbp6-Mediated Bmal1 Ubiquitination Inhibits YAP1 Signaling Pathway to Promote Ferroptosis in Diabetes-Induced Testicular Damage.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1