Designing strategies for eradication of Helicobacter pylori based on prevalence patterns of infection and antibiotic resistance in a low‐income, medically underserved community in the United States

Abstract

Background Regional variation in Helicobacter pylori resistance patterns is a significant contributing factor for the ineffectiveness of traditional treatments. To improve treatment outcomes, we sought to create an individualized, susceptibility‐driven therapeutic approach among our patient population, which is one of the poorest in the nation. It is medically underserved, minority‐predominant and has high incidence of H pylori infection. Methods We compiled various factors involved in the antibiotic resistance of H pylori from literature. We then created a predictive model to customize therapies based on analyzed data from 2,014 H pylori patients with respect to several of these factors. The predictions of the model were further tested with analysis of patient stool samples. Results A clear pattern of H pylori prevalence and antibiotic resistance was observed in our patients. We observed that majority of H pylori patients were women (62%) and over the age of 40 years (80%). 30% and 36% of the H pylori patients were African American and Hispanic, respectively. A median household income of less than $54,000, past H pylori infection, previous use of certain antibiotics for any infection decreased the chance of eradication. Results of the stool testing were consistent with model predictions (90% accuracy). Conclusion This model demonstrates the predictive accuracy of H pylori infection and antibiotic resistance based on patient attributes and previous treatment history. It will be useful to formulate customized treatments with predicted outcomes to minimize failures. Our community attributes may contribute toward broad applicability of model for other similar communities.

Characterization of gut microbiome and metabolome in Helicobacter pylori patients in an underprivileged community in the United States

“…In other words, patients who experience decreased gut microbial diversity as they age, potentially mediated by diet or chronic inflammation, may be more prone to H. pylori infection, particularly by mid-adulthood. In our previous study of 2,014 H pylori patients, we observed that 80% of our patients were above the age of 40 years [23] . Antibiotic exposure is known to alter the gut microbiome, and increased rates of antibiotic resistant- H. pylori among older adults have been attributed to increased antibiotic use compared to younger patients [2, 43, 44] .…”

“…Furthermore, unweighted UniFrac PERMDISP did not reveal differences in dispersion among H. pylori patients versus control subjects ( F = 0.0506, p = 0.809), but weighted UniFrac PERMDISP did ( F = 6.82955, p = 0.017). We also explored the effect of the use of proton pump inhibitors (PPIs) via beta diversity analysis as PPIs are the medications commonly given to H. pylori patients and we had previously noted their influence on the antibiotic resistance of H. pylori [23] . Unweighted UniFrac PERMANOVA identified differences between PPI users and non-users across all participants ( F = 1.939, p = 0.012), though the magnitude of the effect was smaller than that seen for H. pylori infection as described above.…”

“…No. 45630, 45660, Norgen Biotek Corp., Thorold, Ontario, Canada) prior to freezing at –80 o C as described previously [23] .…”

“…Previously, we studied the pattern of H. pylori prevalence and antibiotic resistance in our socioeconomically challenged and underprivileged inner-city community [23] . As a next step, we then sought to characterize the gut microbiome associated with H. pylori infection in our patients.…”

Clinical Factors Implicated in Antibiotic Resistance in Helicobacter pylori Patients

“…for any infection decreased the chance of eradication, while past use of PPI, metronidazole and tetracycline increased the chances of eradication. These results suggest that the clinical history may obviate the need for laboratory testing in predicting resistance [24]. Here we review clinical factors associated with antibiotic resistance that may provide avenues for research to predict which patients are at increased risk of treatment failure and subsequently guide the selection of successful and cost-effective personalized empiric therapy.…”