宿主微生物多组学和型分析对患有炎症性肠病患者未来复发的预后有价值
Jill O'Sullivan1,2,3, Shriram Patel1,2,4, Gabriel E Leventhal5
1School of Microbiology, University College Cork, Cork, Ireland.
Gut microbes
|January 15, 2025
概括
结合宿主和微生物组数据的多组分析,可以预测炎症性肠病 (IBD) 在结肠镜检查后长达四年的复发. 一个患者的型与增加的疾病爆发有关.
科学领域:
- 胃肠病学 胃肠病学
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
背景情况:
- 炎症性肠病 (IBD),包括克罗恩病 (CD) 和性结肠炎 (UC),是不确定的起源的慢性疾病.
- 遗传,免疫和环境因素,特别是肠道微生物组,都与IBD的发病有关.
- 预测疾病复发对于管理IBD患者至关重要.
研究的目的:
- 调查宿主微生物变化是否可以预后分层IBD患者在内镜检查后长达四年的复发风险.
- 评估各种OMIC数据的预测潜力,包括IBD复发的微生物组和宿主因素.
主要方法:
- 分析142名IBD患者 (54名CD,88名UC) 和34名对照患者的粘膜活检数据 (16S rRNA基因,转录片,宿主转录基因,表观基因,遗传学).
- 机器学习方法被用来整合多omics功能,用于复发预测.
- 研究了疾病爆发和患者型之间的关联.
主要成果:
- 单一的omics分析本身无法可靠地区分在四年内复发和缓解的患者.
- 一个患者的型与更高的疾病爆发频率有关.
- 一个多omics机器学习模型,整合微生物组和宿主特征,成功预测了在四年内复发.
结论:
- 多omics方法,整合宿主和微生物群数据,提供优越的预测能力IBD复发相比单个omics分析.
- 患者型是与IBD复发频率增加相关的潜在生物标志物.
- 对IBD患者复发风险的预后分层可以通过使用全面的多omics数据来实现.
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