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引用次数: 0
摘要
越来越多的证据表明,抗生素和许多针对人类的药物可以改变肠道微生物群的组成,但这些影响的持久性尚不清楚。在他们的文章中,Aasmets及其同事(O. Aasmets, N. Taba, K. L. Krigu, R. Andreson等人,mSystems e00541- 25,2025, https://doi.org/10.1128/msystems.00541-25)利用2509个人的电子健康记录(EHR)和粪便宏基因组数据来评估过去用药(采样前5年)对肠道微生物组组成的影响。他们发现,在186种测试药物中,近一半具有长期效果,抗生素、受体阻滞剂、苯二氮卓类衍生物、质子泵抑制剂和抗抑郁药与摄入后持续数年的微生物组变化有关。对于一些药物,效果是加性的,重复使用后观察到更大的影响。总的来说,作者强调了样本收集前几年的药物使用是如何在微生物组研究中经常被忽视的混淆因素,并强调了将电子病历与微生物组数据结合起来评估过去药物使用影响的实用性。
The gut remembers: the long-lasting effect of medication use on the gut microbiome.
Growing evidence suggests that antibiotics and many human-targeted medications can alter the gut microbiome composition, but the persistence of these effects remains unclear. In their article, Aasmets and colleagues (O. Aasmets, N. Taba, K. L. Krigu, R. Andreson, et al., mSystems e00541-25, 2025, https://doi.org/10.1128/msystems.00541-25) leveraged electronic health records (EHR) and stool metagenomic data from 2,509 individuals to assess the impact of past medication use (up to 5 years prior to sampling) on the gut microbiome composition. They found that nearly half of the 186 tested drugs had long-term effects, with antibiotics, beta-blockers, benzodiazepine derivatives, proton-pump inhibitors, and antidepressants associated with microbiome changes that persisted for years after intake. For some medications, the effects were additive, with greater impact observed after repeated use. Overall, the authors highlight how medication use in the years preceding sample collection represents an often overlooked confounding factor in microbiome studies and emphasize the utility of combining EHR with microbiome data to assess the impact of past medication use.
mSystemsBiochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍:
mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.