Gut microbiome research: Revealing the pathological mechanisms and treatment strategies of type 2 diabetes

被引:1
作者
Fang, Xinyi [1 ,2 ]
Zhang, Yanjiao [1 ]
Huang, Xinyue [3 ]
Miao, Runyu [1 ,2 ]
Zhang, Yuxin [1 ]
Tian, Jiaxing [1 ]
机构
[1] China Acad Chinese Med Sci, Guanganmen Hosp, Inst Metab Dis, Beijing 100053, Peoples R China
[2] Beijing Univ Chinese Med, Grad Coll, Beijing, Peoples R China
[3] Changzhi Med Coll, Clin Med Coll 1, Changzhi, Shanxi, Peoples R China
关键词
gut microbial metabolites; gut microbiome research; gut microbiota; machine learning; microbial therapy; type; 2; diabetes; CHAIN FATTY-ACIDS; AKKERMANSIA-MUCINIPHILA; SODIUM-BUTYRATE; DIETARY FIBER; BILE-ACIDS; GLUCOSE; HOST; PROPIONATE; EXPRESSION; CELLS;
D O I
10.1111/dom.16387
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
The high prevalence and disability rate of type 2 diabetes (T2D) caused a huge social burden to the world. Currently, new mechanisms and therapeutic approaches that may affect this disease are being sought. With in-depth research on the pathogenesis of T2D and growing advances in microbiome sequencing technology, the association between T2D and gut microbiota has been confirmed. The gut microbiota participates in the regulation of inflammation, intestinal permeability, short-chain fatty acid metabolism, branched-chain amino acid metabolism and bile acid metabolism, thereby affecting host glucose and lipid metabolism. Interventions focusing on the gut microbiota are gaining traction as a promising approach to T2D management. For example, dietary intervention, prebiotics and probiotics, faecal microbiota transplant and phage therapy. Meticulous experimental design and choice of analytical methods are crucial for obtaining accurate and meaningful results from microbiome studies. How to design gut microbiome research in T2D and choose different machine learning methods for data analysis are extremely critical to achieve personalized precision medicine.
引用
收藏
页码:4051 / 4068
页数:18
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