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Background and aims Proton pump inhibitors PPIs are among the top 10 most widely used drugs in the world. PPI use has been associated with an increased risk of enteric infections, most notably Clostridium difficile. The gut microbiome plays an important role in enteric infections, by resisting or promoting colonisation by pathogens. In this study, we investigated the influence of PPI use on the gut microbiome. Methods The gut microbiome composition of individuals, spanning three cohorts, was assessed by tag sequencing of the 16S rRNA gene. The difference in microbiota composition in PPI users versus non-users was analysed separately in each cohort, followed by a meta-analysis. Results of the participants were using PPIs at the moment of stool sampling. In PPI users we observed a significant increase in bacteria: genera Enterococcus , Streptococcus , Staphylococcus and the potentially pathogenic species Escherichia coli. Conclusions The differences between PPI users and non-users observed in this study are consistently associated with changes towards a less healthy gut microbiome. These differences are in line with known changes that predispose to C. On a population level, the effects of PPI are more prominent than the effects of antibiotics or other commonly used drugs. You will be able to get a quick price and instant permission to reuse the content in many different ways. Changes in the gut microbiome can resist or promote the colonisation of enteric infections. Oral bacteria and potential pathogenic bacteria are increased in the gut microbiota of PPI users. On the population level we see more microbial alterations in the gut associated with PPI use than with antibiotics or other drug use. Given the widespread use of PPI, the morbidity and mortality associated with enteric infections, and the increasing number of studies investigating the microbiome, healthcare practitioners and researchers should take into consideration the influence of PPI on the gut microbiome. Proton pump inhibitors PPIs are among the top 10 most widely used drugs in the world. In the same year, esomeprazole was the second largest drug in terms of revenue in the USA. PPI use has been associated with increased risk of enteric infections. The gut microbiome plays an important role in these enteric infections. We studied the effect of PPI use on the gut microbial composition in three independent cohorts from the Netherlands. These cohorts together comprise adult individuals, including healthy subjects and patients with GI diseases. Cohort 1 consists of individuals who participate in the general population study LifeLines-DEEP in the northern provinces of the Netherlands. Current medication use at the time of stool collection of Cohort 1 participants was extracted from a standardised questionnaire. PPI use was scored if participants used omeprazole, esomeprazole, pantoprazole, lansoprazole, dexlansoprazole or rabeprazole. To exclude other possible drug effects on the gut microbiome, medication use was scored in eight categories, allowing for later correction of parameters or exclusion of certain participants. These categories were medication that: 1 changes bowel movement or stool frequency, 2 lowers triglyceride levels, 3 lowers cholesterol levels, 4 anti-diabetic medication oral and insulin , 5 systemic anti-inflammatory medication excluding NSAIDs , 6 topical anti-inflammatory medication, 7 systemic antibiotics, including antifungal and antimalarial medication, and 8 antidepressants including serotonin-specific reuptake inhibitors SSRIs , serotonin-norepinephrine reuptake inhibitors SNRIs , mirtazapine, and tricyclic antidepressants TCAs. The definitions of these categories are described in the online supplementary appendix. Pseudonymised data for all three cohorts was provided to the researchers. Information on age, gender and body mass index BMI was available for all three cohorts. In Cohort 1, gut complaints were investigated using an extensive questionnaire that included defecation frequency and the Bristol Stool Scale. The patients with IBD in Cohort 2 were diagnosed based on accepted radiological, endoscopic and histopathological evaluation. A total of stool samples and oral cavity mucus samples were collected. Cohorts 1 and 2 used identical protocols to collect the stool samples. Participants of cohort 1 and 2 were asked to collect one stool sample at home. Oral cavity mucus samples were collected from additional healthy volunteers using buccal swabs. To determine the bacterial composition of the stool and oral cavity mucus samples, sequencing of the variable region V4 of the 16S rRNA gene was performed using Illumina MiSeq. DNA isolation is described in the Methods section of the online supplementary appendix. Sequencing and the determination of taxonomy are described in the Methods section of the online supplementary appendix. In each cohort, differentially abundant taxa in the gut microbiome between PPI users and non-PPI users were analysed using the multivariate statistical framework MaAsLin. After running the association studies in the individual cohorts, we performed a meta-analysis of the three cohorts, using the weighted Z-score method. The Cochran's Q test was used to check for heterogeneity. Differences in richness the number of species within a sample , principal coordinate analyses PCoA and Shannon diversity analysis were determined using the QIIME microbiome analysis software. In all the microbiome analyses, multiple test corrections were based on the false discovery rate FDR. An FDR value of 0. In addition to the PPI effect, we also tested the influence of other commonly used drugs in Cohort 1. Using MaAsLin with similar settings to those described above, we tested the microbial changes associated with the use of other drugs, with and without correction for PPI, and the changes when including these common drugs as a correcting factor in the PPI versus non-PPI analysis. Significant results were graphically represented in cladograms using GraPhlAn. Differentially abundant taxa were corrected for several parameters, which were identified by statistical analysis of cohort phenotypes or univariate MaAsLin runs and subsequently added as cofactors to the additive linear model. The analysis of patients with IBD in Cohort 2 was corrected for age, gender, BMI, antibiotics use, sequence read depth, diagnosis Crohn's disease or UC combined with disease location colon, ileum or both and IBD medication use of mesalazines, steroids, thiopurines, methotrexate or anti-TNF antibodies. In the meta-analysis, all microbiome data were corrected for age, gender, BMI, antibiotics use and sequence read depth. PPI were used by Women use PPI more often than men: 9. PPI users were generally older: There was no overlap between PPI users and antibiotics users in Cohort 3. Based on our data, we included age, gender, BMI and antibiotics as cofactors in the microbiome analyses. Table 1 provides an overview of the characteristics per cohort and the use of PPI. The predominant phylum in each cohort was Firmicutes with abundances of Information on the composition of the gut microbiome for all three cohorts and on all taxonomic levels is provided in online supplementary figures S1, S2 and table S1. These changes are depicted in a cladogram in figure 1 and in a heatmap in figure 2 , and in online supplementary figure S5. Details of each taxon, including the individual direction, coefficient, p value and FDR for each cohort, as well as the meta-analysis, are provided in online supplementary tables S2 and S3. Cochran's Q test was used to check for heterogeneity. PPI-associated statistically significant differences in the gut microbiome. Each dot represents a bacterial taxon. The two innermost dots represent the highest level of taxonomy in our data: the kingdoms Archea and Bacteria prokaryotes , followed outwards by the lower levels: phylum, class, order, family, genus and species. Red dots represent significantly increased taxa. Blue dots represent significantly decreased taxa. Significantly altered families in PPI users consistent in three cohorts. Meta-analysis of three independent cohorts comprising faecal samples. The overall difference of the gut microbiome associated with PPI use was also observed in the PCoA of all the data sets together figure 3 and see online supplementary figure S6. The same PCoA with separate colours for each cohort has been added in online supplementary figure S7. Principal coordinate analysis of gut microbiome samples and oral microbiome samples. For Principal Coordinate 1 there is a significant shift of the gut microbiome of PPI users towards the oral microbiome. The order Actinomycetales, families Streptococcoceae , Micrococcoceae , genus Rothia and species Lactobacillus salivarius were increased in participants using PPI in each cohort. We hypothesised that the changes in the gut microbiome associated with PPI use are caused by reduced acidity of the stomach and the subsequent survival of more bacteria that are ingested with food and oral mucus. Indeed, some of the statistically significantly increased bacteria in PPI users eg, Rothia dentocariosa , Rothia mucilaginosa , the genera Scardovia and Actinomyces and the family Micrococcaceae are typically found in the oral microbiome. In online supplementary figure S8, the over-representation of oral cavity bacteria in the guts of PPI users is depicted in a cladogram. Some of the significantly increased taxa were more abundant in the small intestine. In Cohort 1, 16 taxa were associated with antibiotics and other commonly used drug categories besides PPI see online supplementary table S7. After correction for PPI use, only six taxa remained associated with certain drugs: statins, fibrates and drugs that change bowel movements. All 92 alterations in bacterial taxa associated with PPI use remained statistically significant if we correct the microbiome analyses for antibiotics and other commonly used drugs. We show that PPI use is consistently associated with profound changes in the gut microbiome. In our study, these changes were more prominent than changes associated with either antibiotics or other commonly used drugs. Gut microbiota can resist or promote colonisation of C. We hypothesised that PPIs change the gut microbiome through their direct effect on stomach acid. This acidity forms one of the main defenses against the bacterial influx that accompanies ingesting food and oral mucus. PPIs reduce the acidity of the stomach, allowing more bacteria to survive this barrier. We have shown here that species in the oral microbiome are more abundant in the gut microbiome of PPI users. Moreover, a study looking into the effect of PPIs on the oesophageal and gastric microbiome in oesophagitis and Barret's oesophagus showed similar bacterial taxa associated with PPI use, including increased levels of Enterobacteriaceae, Micrococcaceae, Actinomycetaceae and Erysipelotrichaceae. Taxa and microbiome aspects associated with PPI use and increased risk of C. We looked at the role of the gut microbiome in C. This leads to a wide spectrum of symptoms varying from mild diarrhoea to fulminant relapsing diarrhoea and pseudomembranous colitis. The Ruminococcaceae family is significantly decreased in patients with C. Species of the Bifidobacterium genus: Bifidobacterium longum , Bifidobacterium lactis , Bifidobacterium pseudocatenulatum , Bifidobacterium breve , Bifidobacterium pseudolongum , Bifidobacterium adolescentis and Bifidobacterium animalis lactis have been shown to inhibit or prevent C. The class Gammaproteobacteria and the family Enterobacteriaceae are s ignificantly increased in PPI users. Gammaproteobacteria are enriched in patients with C. Those mice that became clinically ill after the administration of an antibiotic cocktail containing clindamycin and a C. The increased abundance of the family Lactobacillaceae in PPI users was associated with increased risk of C. Mice treated with a cocktail of antibiotics consisting of kanamycin, gentamycin, colistin, metronidazole and vancomycin , cefoperazone or a combination of clindamycin and cefoperazone have higher levels of Lactobacillaceae in their gut. Last, the Veillonella genus that is increased in PPI users is significantly enriched in patients with C. The prevention of healthcare-associated C. Future microbiome studies in humans should therefore always take the effect of PPI on the gut microbiome into account. This paper reports the largest study to date investigating the influence of PPI on the gut microbiome. The profound alterations seen in the gut microbiome could be linked to the increased risk of C. Given the widespread use of PPI, the morbidity and mortality associated with enteric infections, and the increasing number of studies investigating the microbiome, healthcare practitioners and microbiome researchers should be fully aware of the influence of PPI on the gut microbiome. This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author s and has not been edited for content. Provenance and peer review Not commissioned; externally peer reviewed. Skip to main content. Log in via OpenAthens. Log in using your username and password For personal accounts OR managers of institutional accounts. Forgot your log in details? Register a new account? Forgot your user name or password? Search for this keyword. Advanced search. Log in via Institution. Email alerts. Article Text. Article menu. Gut microbiota. Original article. Proton pump inhibitors affect the gut microbiome. Box Abstract Background and aims Proton pump inhibitors PPIs are among the top 10 most widely used drugs in the world. Statistics from Altmetric. PPI is one of the most commonly used drugs. How might it impact on clinical practice in the foreseeable future? Methods Cohorts We studied the effect of PPI use on the gut microbial composition in three independent cohorts from the Netherlands. Medication use Current medication use at the time of stool collection of Cohort 1 participants was extracted from a standardised questionnaire. Gut complaints and other clinical characteristics Information on age, gender and body mass index BMI was available for all three cohorts. Stool and oral cavity mucus sample collection A total of stool samples and oral cavity mucus samples were collected. Statistical analysis In each cohort, differentially abundant taxa in the gut microbiome between PPI users and non-PPI users were analysed using the multivariate statistical framework MaAsLin. Correction for factors influencing the gut microbiota Differentially abundant taxa were corrected for several parameters, which were identified by statistical analysis of cohort phenotypes or univariate MaAsLin runs and subsequently added as cofactors to the additive linear model. View this table: View inline View popup. Table 1 Characteristics of the three independent cohorts in this study. Composition of the gut microbiota The predominant phylum in each cohort was Firmicutes with abundances of Figure 1 PPI-associated statistically significant differences in the gut microbiome. Figure 2 Significantly altered families in PPI users consistent in three cohorts. Figure 3 Principal coordinate analysis of gut microbiome samples and oral microbiome samples. Similar changes in three independent cohorts were associated with PPI use The order Actinomycetales, families Streptococcoceae , Micrococcoceae , genus Rothia and species Lactobacillus salivarius were increased in participants using PPI in each cohort. Oral cavity bacteria are more abundant in the gut microbiome of PPI users We hypothesised that the changes in the gut microbiome associated with PPI use are caused by reduced acidity of the stomach and the subsequent survival of more bacteria that are ingested with food and oral mucus. PPI use is independent of bowel movement frequency and stool consistency Some of the significantly increased taxa were more abundant in the small intestine. PPI, antibiotics and other commonly used drugs In Cohort 1, 16 taxa were associated with antibiotics and other commonly used drug categories besides PPI see online supplementary table S7. Conclusions We show that PPI use is consistently associated with profound changes in the gut microbiome. Data and Facts on Top sales in the United States in A proton-pump inhibitor expedition: the case histories of omeprazole and esomeprazole. Nat Rev Drug Discov ; 2 : — 9. Moayyedi P , Talley NJ. Gastro-oesophageal reflux disease. Lancet ; : — Continuous proton pump inhibitor therapy and the associated risk of recurrent clostridium difficile infection. Kelly OB , Dillane C , Patchett SE , et al , The Inappropriate prescription of oral proton pump inhibitors in the hospital setting: a prospective cross-sectional study. Dig Dis Sci ; 60 : — 6. Potential association between the recent increase in campylobacteriosis incidence in the Netherlands and proton-pump inhibitor use—an ecological study. Eurosurveillance ; 19 : 1 — 6. Systematic review of the risk of enteric infection in patients taking acid suppression. Am J Gastroenterol ; : — Microbiota-mediated colonization resistance against intestinal pathogens. Nat Rev Immunol. Control of pathogens and pathobionts by the gut microbiota. Nat Immunol ; 14 : — Britton RA. Role of the intestinal microbiota in resistance to colonization by Clostridium difficile. Gastroenterology ; : — Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature ; : — 8. Duodenal infusion of donor feces for recurrent Clostridium difficile. N Engl J Med ; : — Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open ; 5 : e Cohort Profile: LifeLines, a three-generation cohort study and biobank. Int J Epidemiol ; 44 : — Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol ; 13 : R Nat Publ Gr ; 7 : — 6. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ ; 3 : e Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Gastric microbiota is altered in oesophagitis and Barrett's oesophagus and further modified by proton pump inhibitors. Environ Microbiol ; 16 : — Human gut microbiota in obesity and after gastric bypass. Burden of Clostridium difficile Infection in the United States. Multistate point-prevalence survey of health care—associated infections. Clostridium difficile infection: new developments in epidemiology and pathogenesis. Nat Rev Microbiol ; 7 : — The interplay between microbiome dynamics and pathogen dynamics in a murine model of Clostridium difficile infection. Gut Microbes ; 2 : — Decreased diversity of the fecal Microbiome in recurrent Clostridium difficile-associated diarrhea. J Infect Dis ; : — 8. Intestinal dysbiosis and depletion of butyrogenic bacteria in Clostridium difficile infection and nosocomial diarrhea. J Clin Microbiol ; 51 : — Clostridium difficile carriage in elderly subjects and associated changes in the intestinal microbiota. J Clin Microbiol ; 50 : — F and MHW. Mixed infection by Clostridium difficile in an in vitro model of the human gut. J Antimicrob Chemother ; 68 : — Microbiome data distinguish patients with clostridium difficile infection and non- C. MBio ; 5 : e — OpenUrl PubMed. Department H and HS. Food and Drug Administration. Blaser M. Antibiotic overuse: Stop the killing of beneficial bacteria. Nature ; : — 4. Mehal WZ. Nat Rev Gastroenterol Hepatol ; 10 : — Human Genetics Shape the Gut Microbiome. Cell ; : — Supplementary materials Supplementary Data This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author s and has not been edited for content. Data supplement 1 - Online appendix Data supplement 2 - Online table 1 Data supplement 3 - Online table 2 Data supplement 4 - Online table 3 Data supplement 5 - Online table 4 Data supplement 6 - Online table 5 Data supplement 7 - Online table 6 Data supplement 8 - Online table 7. Competing interests None declared. 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