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, 11 (1), 362

Impact of Commonly Used Drugs on the Composition and Metabolic Function of the Gut Microbiota

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Impact of Commonly Used Drugs on the Composition and Metabolic Function of the Gut Microbiota

Arnau Vich Vila et al. Nat Commun.

Abstract

The human gut microbiota has now been associated with drug responses and efficacy, while chemical compounds present in these drugs can also impact the gut bacteria. However, drug-microbe interactions are still understudied in the clinical context, where polypharmacy and comorbidities co-occur. Here, we report relations between commonly used drugs and the gut microbiome. We performed metagenomics sequencing of faecal samples from a population cohort and two gastrointestinal disease cohorts. Differences between users and non-users were analysed per cohort, followed by a meta-analysis. While 19 of 41 drugs are found to be associated with microbial features, when controlling for the use of multiple medications, proton-pump inhibitors, metformin, antibiotics and laxatives show the strongest associations with the microbiome. We here provide evidence for extensive changes in taxonomy, metabolic potential and resistome in relation to commonly used drugs. This paves the way for future studies and has implications for current microbiome studies by demonstrating the need to correct for multiple drug use.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Microbial richness (Shannon index) for each participant stratified per number of medications used.
Dots represent the richness value per each sample in the study. Boxplot shows the median and interquartile range (25th and 75th). Whiskers show the 1.5*IQR range. Black lines show linear regression with a purple shadow indicating the 95% confidence interval. From left to right, the IBD cohort (N = 454 samples, linear regression, coefficient = −0,001, p = 0.88), population cohort (N = 1124 samples, linear regression, coefficient = −0.002, p = 0.77) and the IBS cohort (N = 305 samples, linear regression, coefficient = −0.016, p = 0.06). (Source data are provided as a Source Data file).
Fig. 2
Fig. 2. Overview of the number of associated microbial features.
a,b Bar-plots showing the number of associations between each type of drug and microbial taxa a and microbial pathways b. Bar colours indicate the results from the separate cohorts and the results from the meta-analyses. Black bars indicate the population cohort, dark grey bars the IBS cohort, light grey bars the IBD cohort and red bars the results from the meta-analyses. The single-drug model shows the association when considering one drug at the time while taking age, sex and BMI into account. The multi-drug model considers the use of multiple drug types while taking age, sex and BMI into account. (Source data are provided as a Source Data file).
Fig. 3
Fig. 3. Correlation between the relative abundance of Methanobrevibacter smithii and the pathways associated with oral steroids.
Dots are coloured by study cohort. In blue the cohort of IBD patients, in yellow the population cohort and in grey the IBS case-control cohort. X-axis represents the relative abundance of Methanobrevibacter smithii and Y-axis the read per kilobase (RPK) of each pathway. Spearman correlation was used to calculate the correlation and significance. (Source data are provided as a Source Data file).
Fig. 4
Fig. 4. Microbial contribution to the purine deoxyribonucleoside degradation pathway.
Box plots represent the relative contribution of each microbe to the overall pathway quantification for each cohort separately. On top is the IBD cohort represented, in the middle the IBS cohort and on the bottom row the LLD cohort. Blue box-plots represent the values of PPI users. Red box-plots represent the values of non-PPI users. Asterisks indicate statistically significant differences between PPI users and non-users (Wilcoxon test, FDR < 0.05). Box plots show medians and the first and third quartiles (the 25th and 75th percentiles), respectively. The upper and lower whiskers extend the largest and smallest value no further than 1.5*IQR, respectively. Outliers are plotted individually.

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