Association between proton pump inhibitor therapy and clostridium difficile infection: a contemporary systematic review and meta-analysis.

Abstract Introduction Emerging epidemiological evidence suggests that proton pump inhibitor (PPI) acid-suppression therapy is associated with an increased risk of Clostridium difficile infection (CDI). Methods Ovid MEDLINE, EMBASE, ISI Web of Science, and Scopus were searched from 1990 to January 2012 for analytical studies that reported an adjusted effect estimate of the association between PPI use and CDI. We performed random-effect meta-analyses. We used the GRADE framework to interpret the findings. Results We identified 47 eligible citations (37 case-control and 14 cohort studies) with corresponding 51 effect estimates. The pooled OR was 1.65, 95% CI (1.47, 1.85), I2 = 89.9%, with evidence of publication bias suggested by a contour funnel plot. A novel regression based method was used to adjust for publication bias and resulted in an adjusted pooled OR of 1.51 (95% CI, 1.26–1.83). In a speculative analysis that assumes that this association is based on causality, and based on published baseline CDI incidence, the risk of CDI would be very low in the general population taking PPIs with an estimated NNH of 3925 at 1 year. Conclusions In this rigorously conducted systemic review and meta-analysis, we found very low quality evidence (GRADE class) for an association between PPI use and CDI that does not support a cause-effect relationship

Findings during screening colonoscopies in a Middle Eastern cohort

Background/Aims: Colorectal cancer is the most common cancer in males and the third most common cancer in females. We aim to determine the polyp and adenoma prevalence in a cohort of patients who underwent opportunistic screening colonoscopies. Patients and Methods: A retrospective cohort study was conducted using an endoscopic reporting database of individuals seen at three tertiary care hospitals (two public hospitals and one private) in Riyadh, Saudi Arabia. Consecutive patients who were 45 years of age and older and underwent opportunistic screening colonoscopies between November 2016 and October 2017 were included. We excluded those with a history of colon cancer or colonic resection for any reason, inflammatory bowel disease, gastrointestinal bleeding, or anemia. Results: Around 1180 patients were included in the study with a mean age of 58.6 years (SD = 7.3), with males representing 53.6% and an overall cecal intubation rate of 92.4%. Masses were found in 1.6% of the study population (50% in the sigmoid or rectosigmoid, 37.5% in the rectum). The polyp detection rate in colonoscopies was 24.8% and the adenoma detection rate was 16.8%. The histology of removed polyps was tubular adenomas in 56.6%, hyperplastic polyps in 32.7%, tubulovillous adenomas in 8.2%, and villous adenomas in 2.5%. The majority of the polyps were in the sigmoid colon (28.3%) and rectum (22.0%), followed by the ascending colon (11.2%) and cecum (10.3%), then the transverse colon and descending colon (9.4% each), and multiple locations in the remainder. Conclusion: The prevalence of polyps and adenomas in this cohort is less than that reported in the Western populations

Colonic Mucosal Microbiota in Colorectal Cancer: A Single-Center Metagenomic Study in Saudi Arabia

Background and Aim. Because genetic and geographic variations in intestinal microbiota are known to exist, the focus of this study was to establish an estimation of microbiota in colorectal cancer (CRC) patients in Saudi Arabia by means of metagenomic studies. Methods. From July 2010 to November 2012, colorectal cancer patients attending our hospital were enrolled for the metagenomic studies. All underwent clinical, endoscopic, and histological assessment. Mucosal microbiota samples were collected from each patient by jet-flushing colonic mucosa with distilled water at unified segments of the colon, followed by aspiration, during colonoscopy. Total purified dsDNA was extracted and quantified prior to metagenomic sequencing using an Illumina platform. Satisfactory DNA samples (n=29) were subjected to metagenomics studies, followed by comprehensive comparative phylogenetic analysis. An equal number of healthy age-matched controls were also examined for colonic mucosal microbiota. Results. Metagenomics data on 29 patients (14 females) in the age range 38–77 years were analyzed. The majority 11 (37%) of our patients were overweight (BMI = 25–30). Rectal bleeding was the presenting symptom in 18/29 (62%), while symptomatic anemia was the presenting symptom in 11/29 (37%). The location of colon cancer was rectal in 14 (48%), while cecal growth was observed in 8 (27%). Hepatic flexure growth was found in 1 (3%), descending colonic growth was found in 2 (6%), and 4 (13%) patients had transverse colon growth. The metagenomics analysis was carried out, and a total of 3.58G reads were sequenced, and about 321.91G data were used in the analysis. This study identified 11 genera specific to colorectal cancer patients when compared to genera in the control group. Bacteroides fragilis and Fusobacterium were found to be significantly prevalent in the carcinoma group when compared to the control group. Conclusion. The current study has given an insight into the microbiota of colorectal cancer patients in Saudi Arabia and has identified various genera significantly present in these patients when compared to those of the control group

Summary of reviews of the association between PPI use and <i>Clostridium Difficile</i> infection.

<p>Legend: PPI: Proton pump inhibitor; NA: Not available.</p

Influence of study type, country, weather effect estimate adjusted or not and PPI ascertainment method on the pooled effect estimate and its associated heterogeneity.

<p>Influence of study type, country, weather effect estimate adjusted or not and PPI ascertainment method on the pooled effect estimate and its associated heterogeneity.</p

Characteristics of the Included Studies.

<p>♦ NA: Data obtained from abstract</p><p>Legend: OR: odds ratio; HR: harzard ratio; CDAD: <i>Clostridium difficile-</i>associated diarrhea; PPI: Proton pump inhibitor; H2RA: VA: Veteran Affairs; D: Durham County, P: Prospective; R: Retrospective, ABX: Antibiotic; GPRD: General Practice Research Database; ICU: Intensive care unit</p><p>Metabolic processes are controlled by a variety of enzymes. For instance, cytochrome P450 monooxygenase could detoxify herbicides such as fenoxaprop-ethyl, diclofop-methyl, and bentazon in plants <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050836#pone.0050836-Deshpande1" target="_blank">[22]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050836#pone.0050836-Bavishi1" target="_blank">[23]</a>. Polyphenol oxidase (PPO), commonly found in fungi and plants, refers to a group of enzymes that catalyze the oxidation of phenolic compounds <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050836#pone.0050836-Stroup1" target="_blank">[24]</a>. Peroxidase (POD), another type of oxidative enzyme commonly present in plant and animal tissues, can oxidize phenols and aromatic amines in the presence of hydrogen peroxide. In contrast, the oxidation of phenolic compounds by PPO requires the presence of oxygen gas <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050836#pone.0050836-Liberati1" target="_blank">[25]</a>. Both PPO and POD play important roles in the metabolism of aromatic compounds in soil and water <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050836#pone.0050836-Guyatt1" target="_blank">[26]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050836#pone.0050836-Wells1" target="_blank">[27]</a>. However, little information is available regarding their function in the metabolism of PAHs by plants.</p

Contour enhanced funnel plot of the association between the effect-estimates and its standard errors:

<p>* Contour enhanced funnel plots with implementation of regression adjustment model (adjusted effect at top where SE is 0).* The contour lines differentiate the significance and non-significance regions in the plot at 1%, 5% and 10% significance levels. *Vertical lines show average effect-estimates from random effect (red), and fixed effect models (blue). *A regression line (black) is added for regression based adjustment (With adjusted effect estimate and 95% CI at top where SE is 0). Abbreviations: FEMA: Fixed effect meta-analysis, REMA: Random effect meta-analysis, Reg: Regression line.</p

Forest Plot of the Meta analyses of The Association Between CDI and Proton Pump Inhibitors Based on 51 Observations.

<p>Forest Plot of the Meta analyses of The Association Between CDI and Proton Pump Inhibitors Based on 51 Observations.</p