A hidden confounder for microbiome studies: medications used years before sample collection
Oliver Aasmets et al.
Medication usage is a known contributor to the inter-individual variability of the gut microbiome. However, medications are often used repeatedly and for long periods, a notion yet unaccounted for in microbiome studies. Recently, we and others showed that not only the usage of antibiotics and antidepressants at sampling, but also past consumption, is associated with the gut microbiome. This effect can be “additive”—the more a medication is used, the stronger the impact on the microbiome. Here, by utilizing retrospective medication usage data from the electronic health records and the observational Estonian microbiome cohort shotgun metagenomics data set (n = 2,509), we systematically evaluate the long-term effects of antibiotics and human-targeted medications on the gut microbiome. We show that past usage of medications is associated with the gut microbiome. For example, the effects of antibiotics, psycholeptics, antidepressants, proton pump inhibitors, and beta-blockers are detectable several years after use. Furthermore, by analyzing a subcohort (n = 328) with a second microbiome characterization, we show that similar changes in the gut microbiome occur after treatment initiation or discontinuation, possibly indicating causal effects.
IMPORTANCE
This is the first study using detailed retrospective medication usage data from electronic health records to systematically assess the long-term effects of medication usage on the gut microbiome. We identified carryover and additive effects on the gut microbiome for a range of antibiotics and non-antibiotic medications, such as benzodiazepine derivatives, antidepressants and glucocorticoids, among others. These findings highlight a collateral effect of diverse drug classes on the gut microbiome, which warrants accounting for long-term medication usage history when assessing disease-microbiome associations.
Fig 1 Drug usage in the EstMB cohort. (a) Overview of the EstMB cohort and phenotype data available through the electronic health records (EHR). We aimed to analyze the direct effects of drug usage at the time of sampling (Q1), carryover effects induced by past drug usage (Q2), and additive effects arising from the differing amounts of drugs used in the past (Q3). In a subcohort of the EstMB (n = 328) that provided an additional stool sample (T2), we analyzed the alterations in the microbiome attributable to treatment initiation (Q4). (b) Total number of different drugs used in the EstMB at different ATC levels. (c) Distribution of the number of drugs used by the participants at T1 across different ATC levels. (d) The number of drug users at T1 on ATC4 level; drugs with at least 50 users at T1 are shown. Drugs are ordered according to the number of active drug users. Antibiotics are highlighted in bold. ATC, Anatomical Therapeutic Chemical classification; Q, study question.
LEGGI TUTTO https://journals.asm.org/doi/10.1128/msystems.00541-25#:~:text=https%3A//doi.org/10.1128/msystems.00541%2D25
