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张家超等:基于肠道菌群标志物诊断格雷夫斯病

时间:2021-06-11 08:11来源:热心肠日报 作者:创作:徐硕 审核:mil 浏览次数:
  ①格雷夫斯病(GD)是一种自身免疫病,临床诊断存在不便和困难;

  ②招募162名受试者(健康对照、GD轻症和重症),用鸟枪法宏基因测序分析肠道菌群特征;

  ③GD患者肠道菌群失调,病原菌和产有害代谢物的条件致病菌增多,菌群多样性、有益共生菌和有机酸生成减少,一些菌种的SNP改变;

  ④用随机森林方法建立含32个菌群标志物(涵盖菌种、MAG、基因和SNP)的分类模型,可准确区分3组受试者,并在多队列验证中区分GD与其他疾病(包括帕金森病)。

  主编推荐语                     
  
  mildbreeze

  格雷夫斯病(GD)又称“毒性弥漫性甲状腺肿”,是一种自身免疫疾病的多系统综合征,通常引起甲亢。目前GD的临床诊断较为耗时,且可能与帕金森病混淆,因此需要研发便捷、准确的诊断方法。海南大学张家超团队、Kaining Chen团队与Rob Knight团队合作,在ISME Journal发表文章,对GD患者肠道菌群特征进行分析,鉴定出具有疾病特异性的GD菌群标志物,构建了疾病诊断模型,具有临床应用潜力。
 
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[IF:9.18]
Compositional and genetic alterations in Graves’ disease gut microbiome reveal specific diagnostic biomarkers

格雷夫斯病肠道微生物组的组成和遗传改变揭示特定的诊断生物标志物

10.1038/s41396-021-01016-7

06-02, Article

Abstract:


  Graves’ Disease is the most common organ-specific autoimmune disease and has been linked in small pilot studies to taxonomic markers within the gut microbiome. Important limitations of this work include small sample sizes and low- resolution taxonomic markers. Accordingly, we studied 162 gut microbiomes of mild and severe Graves’ disease (GD) patients and healthy controls. Taxonomic and functional analyses based on metagenome-assembled genomes (MAGs) and MAG-annotated genes, together with predicted metabolic functions and metabolite profiles, revealed a well-defined network of MAGs, genes and clinical indexes separating healthy from GD subjects. A supervised classification model identified a combination of biomarkers including microbial species, MAGs, genes and SNPs, with predictive power superior to models from any single biomarker type (AUC =0.98). Global, cross-disease multi-cohort analysis of gut microbiomes revealed high specificity of these GD biomarkers, notably discriminating against Parkinson’s Disease, and suggesting that non-invasive stool-based diagnostics will be useful for these diseases.

First Authors:

Qiyun Zhu,Qiangchuan Hou,Shi Huang,Qianying Ou,Dongxue Huo

Correspondence Authors:

Kaining Chen,Rob Knight,Jiachao Zhang

All Authors:

Qiyun Zhu,Qiangchuan Hou,Shi Huang,Qianying Ou,Dongxue Huo,Yoshiki Vázquez-Baeza,Chaoping Cen,Victor Cantu,Mehrbod Estaki,Haibo Chang,Pedro Belda-Ferre,Ho Cheol Kim,Kaining Chen,Rob Knight,Jiachao Zhang




 
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