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调整年龄可提高多种疾病中肠道微生物组改变的识别能力。

Adjusting for age improves identification of gut microbiome alterations in multiple diseases.

机构信息

APC Microbiome Ireland, University College Cork, Cork, Ireland.

School of Microbiology, University College Cork, Cork, Ireland.

出版信息

Elife. 2020 Mar 11;9:e50240. doi: 10.7554/eLife.50240.

DOI:10.7554/eLife.50240
PMID:32159510
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7065848/
Abstract

Interaction between disease-microbiome associations and ageing has not been explored in detail. Here, using age/region-matched sub-sets, we analysed the gut microbiome differences across five major diseases in a multi-cohort dataset constituting more than 2500 individuals from 20 to 89 years old. We show that disease-microbiome associations display specific age-centric trends. Ageing-associated microbiome alterations towards a disease-like configuration occur in colorectal cancer patients, thereby masking disease signatures. We identified a microbiome disease response shared across multiple diseases in elderly subjects that is distinct from that in young/middle-aged individuals, but also a novel set of taxa consistently gained in disease across all age groups. A subset of these taxa was associated with increased frailty in subjects from the ELDERMET cohort. The relevant taxa differentially encode specific functions that are known to have disease associations.

摘要

疾病-微生物组关联与衰老之间的相互作用尚未得到详细探讨。在这里,我们使用年龄/地区匹配的子数据集,在一个由 2500 多名 20 至 89 岁的个体组成的多队列数据集内分析了五大疾病的肠道微生物组差异。我们表明,疾病-微生物组关联呈现出特定的以年龄为中心的趋势。在结直肠癌患者中,与衰老相关的微生物组朝着类似于疾病的状态发生改变,从而掩盖了疾病特征。我们在老年患者中发现了一种跨多种疾病共享的微生物组疾病反应,与年轻/中年患者的反应不同,但也存在一组在所有年龄组中都与疾病相关的新型分类群。这些分类群的一部分与 ELDERMET 队列中受试者的脆弱性增加有关。相关分类群差异表达特定的功能,这些功能与疾病相关。

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Front Nutr. 2025 May 29;12:1606922. doi: 10.3389/fnut.2025.1606922. eCollection 2025.
5
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