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Background: Global prescription drug use has been increasing continuously for decades. The gut microbiome, a key contributor to health status, can be altered by prescription drug use, as antibiotics have been repeatedly described to have both short-term and long-standing effects on the intestinal microbiome., Aim: To summarise current findings on non-antibiotic prescription-induced gut microbiome changes, focusing on the most frequently prescribed therapeutic drug categories., Methods: We conducted a systematic review by first searching in online databases for indexed articles and abstracts in accordance with PRISMA guidelines. Studies assessing the intestinal microbiome alterations associated with proton pump inhibitors (PPIs), metformin, nonsteroidal anti-inflammatory drugs (NSAIDs), opioids, statins and antipsychotics were included. We only included studies using culture-independent molecular techniques., Results: Proton pump inhibitors and antipsychotic medications are associated with a decrease in α diversity in the gut microbiome, whereas opioids were associated with an increase in α diversity. Metformin and NSAIDs were not associated with significant changes in α diversity. β diversity was found to be significantly altered with all drugs, except for NSAIDs. PPI use was linked to a decrease in Clotridiales and increase in Actinomycetales, Micrococcaceae and Streptococcaceae, which are changes previously implicated in dysbiosis and increased susceptibility to Clostridium difficile infection. Consistent results showed that PPIs, metformin, NSAIDs, opioids and antipsychotics were either associated with increases in members of class Gammaproteobacteria (including Enterobacter, Escherichia, Klebsiella and Citrobacter), or members of family Enterococcaceae, which are often pathogens isolated from bloodstream infections in critically ill patients. We also found that antipsychotic treatment, usually associated with an increase in body mass index, was marked by a decreased ratio of Bacteroidetes:Firmicutes in the gut microbiome, resembling trends seen in obese patients., Conclusions: Non-antibiotic prescription drugs have a notable impact on the overall architecture of the intestinal microbiome. Further explorations should seek to define biomarkers of dysbiosis induced by specific drugs, and potentially tailor live biotherapeutics to counter this drug-induced dysbiosis. Many other frequently prescribed drugs should also be investigated to better understand the link between these drugs, the microbiome and health status., (© 2017 John Wiley & Sons Ltd.)

Background: Clostridium difficile infection (CDI) may not respond to initial therapy and frequently recurs, but predictors of response and recurrence are inconsistent. The impact of specific alterations in the gut microbiota determining treatment response and recurrence in patients with CDI is unknown., Aim: To assess microbial signatures as predictors of treatment response and recurrence in CDI., Methods: Pre-treatment stool samples and clinical metadata including outcomes were collected prospectively from patients with their first CDI episode. Next generation 16s rRNA sequencing using MiSeq Illumina platform was performed and changes in microbial community structure were correlated with CDI outcomes., Results: Eighty-eight patients (median age 52.7 years, 60.2% female) were included. Treatment failure occurred in 12.5% and recurrence after response in 28.5%. Patients who responded to treatment had an increase in Ruminococcaceae, Rikenellaceae, Clostridiaceae, Bacteroides, Faecalibacterium and Rothia compared to nonresponders. A risk-index built from this panel of microbes differentiated responders (mean 0.07 ± 0.24) from nonresponders (0.52 ± 0.42; P = 0.0002). Receiver operating characteristic (ROC) curve demonstrated that risk-index was a strong predictor of treatment response with an area under the curve (AUC) of 0.85. Among clinical parameters tested, only proton pump inhibitor use predicted recurrent CDI (OR 3.75, 95% CI 1.27-11.1, P = 0.01). Patients with recurrent CDI had statistically significant increases in Veillonella, Enterobacteriaceae, Streptococci, Parabacteroides and Lachnospiraceae compared to patients without recurrence and a risk index was able to predict recurrence (AUC = 0.78)., Conclusion: Gut microbiota signatures predict treatment response and recurrence potentially, allowing identification of patients with Clostridium difficile infection that may benefit from early institution of alternate therapies., (© 2016 John Wiley & Sons Ltd.)

Background: Chemotherapy is commonly used as myeloablative conditioning treatment to prepare patients for haematopoietic stem cell transplantation (HSCT). Chemotherapy leads to several side effects, with gastrointestinal (GI) mucositis being one of the most frequent. Current models of GI mucositis pathophysiology are generally silent on the role of the intestinal microbiome., Aim: To identify functional mechanisms by which the intestinal microbiome may play a key role in the pathophysiology of GI mucositis, we applied high-throughput DNA-sequencing analysis to identify microbes and microbial functions that are modulated following chemotherapy., Methods: We amplified and sequenced 16S rRNA genes from faecal samples before and after chemotherapy in 28 patients with non-Hodgkin's lymphoma who received the same myeloablative conditioning regimen and no other concomitant therapy such as antibiotics., Results: We found that faecal samples collected after chemotherapy exhibited significant decreases in abundances of Firmicutes (P = 0.0002) and Actinobacteria (P = 0.002) and significant increases in abundances of Proteobacteria (P = 0.0002) compared to samples collected before chemotherapy. Following chemotherapy, patients had reduced capacity for nucleotide metabolism (P = 0.0001), energy metabolism (P = 0.001), metabolism of cofactors and vitamins (P = 0.006), and increased capacity for glycan metabolism (P = 0.0002), signal transduction (P = 0.0002) and xenobiotics biodegradation (P = 0.002)., Conclusions: Our study identifies a severe compositional and functional imbalance in the gut microbial community associated with chemotherapy-induced GI mucositis. The functional pathways implicated in our analysis suggest potential directions for the development of intestinal microbiome-targeted interventions in cancer patients., (© 2015 John Wiley & Sons Ltd.)

Background: Gastrointestinal mucositis is defined as inflammation and/or ulcers of the gastrointestinal tract occurring as a complication of chemotherapy and radiation therapy, and affects about 50% of all cancer patients., Aim: To assess the role of gut microbiota in the pathogenesis of gastrointestinal mucositis and the potential for manipulations of the microbiota to prevent and to treat mucositis., Methods: Search of the literature published in English using Medline, Scopus and the Cochrane Library, with main search terms 'intestinal microbiota', 'bacteremia', 'mucositis', 'chemotherapy-induced diarrhoea', 'chemotherapy-induced mucositis', 'radiotherapy-induced mucositis'., Results: The gut microbiota plays a major role in the maintenance of intestinal homoeostasis and integrity. Patients receiving cytotoxic and radiation therapy exhibit marked changes in intestinal microbiota, with most frequently, decrease in Bifidobacterium, Clostridium cluster XIVa, Faecalibacterium prausnitzii, and increase in Enterobacteriaceae and Bacteroides. These modifications may contribute to the development of mucositis, particularly diarrhoea and bacteraemia. The prevention of cancer therapy-induced mucositis by probiotics has been investigated in randomised clinical trials with some promising results. Three of six trials reported a significantly decreased incidence of diarrhoea. One trial reported a decrease in infectious complications., Conclusions: The gut microbiota may play a major role in the pathogenesis of mucositis through the modification of intestinal barrier function, innate immunity and intestinal repair mechanisms. Better knowledge of these effects may lead to new therapeutic approaches and to the identification of predictive markers of mucositis., (© 2014 John Wiley & Sons Ltd.)