The Gut Microbiome as a Fingerprint of Antibiotic Use History

14 Apr 20264 min read0 viewsJournal Feed

GIST

Antibiotic use over the past 8 years was associated with alterations in the gut microbiome composition in 14,979 individuals. These analyses integrated data from the Swedish National Prescribed Drug Register and stool metagenomic profiles.

Alterations were found even after a single antibiotic course and varied markedly across different antibiotic classes. Access to this article via Institution of Civil Engineers Library is not available.

Palleja, A. et al.

Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat.

Microbiol. 3 , 1255–1265 (2018).

This intervention study shows that a combination of broad-spectrum antibiotics produces microbiome alterations that persist for at least 180 days. Fishbein, S.

R. S.

et al. Antibiotic perturbations to the gut microbiome.

Nat. Rev.

Microbiol. 21 , 772–788 (2023).

A review article on the effect of antibiotics on the gut microbiome, strategies to mitigate these effects, and future directions. Nuotio, J.

et al. Use of antibiotics and risk of type 2 diabetes, overweight and obesity: the Cardiovascular Risk in Young Finns Study and the national FINRISK study.

BMC Endocr. Disord.

22 , 284 (2022).

Clinical Editorial

Scope and design

The report synthesizes data from 14,979 individuals integrating longitudinal prescription records with stool metagenomic profiles.
It examines associations between antibiotic use in the preceding eight years and gut microbiome composition, drawing on the Swedish National Prescribed Drug Register linked to microbiome data.
The analysis aims to characterize whether microbiome alterations accompany antibiotic exposure and how these alterations vary by antibiotic class, including in the context of single-course exposure.

Key observational signal

Antibiotic use over the eight-year window is associated with detectable alterations in the composition of the gut microbiome.
These microbiome changes were observed even after a single antibiotic course, indicating that modest exposure can correspond to measurable microbial shifts.
The magnitude and pattern of alterations differed across antibiotic classes, suggesting class-specific microbiome perturbations.

Contextual data and related findings

Prior experimental work is cited to illustrate that broad-spectrum antibiotic regimens can induce microbiome changes persisting for several months, up to at least 180 days, in healthy adults.
Review articles are referenced to discuss broader themes around antibiotic-induced perturbations, potential mitigation strategies, and conceptual frameworks for future work.
Additional population studies are noted that link antibiotic exposure to health outcomes such as type 2 diabetes and cardiovascular events, though these refer to separate cohorts and analyses beyond the microbiome-focused dataset.

Population- and data-related notes

The Swedish Prescribed Drug Register provides the pharmacologic exposure data, enabling pharmacoepidemiological investigation in a large, real-world population.
The source emphasizes the opportunity afforded by this register for linking prescription histories with microbiome profiles, highlighting methodological context for such integrative studies.

Limitations and scope of conclusions

The summary explicitly frames the findings as associations between antibiotic exposure and microbiome composition, without asserting causal inferences or clinical outcomes within this report.
Quantitative results (exact effect sizes, statistical confidence, or specific taxa or functional shifts) are not detailed in the provided content.
The source notes that some referenced studies show longer persistence of microbiome alterations after certain regimens, but the current synthesis focuses on association signals in the larger population and their dependence on antibiotic class, rather than providing granular longitudinal trajectories.

Uncertainty and open questions

It remains unclear from the summary whether reversibility timelines or recovery trajectories are uniform across individuals or antibiotic classes within this dataset.