Helicobacter pylori may be the most successful pathogen in human history. While not as deadly as the bacteria that cause tuberculosis, cholera, and the plague, it infects more people than all the others combined. H. pylori, which migrated out of Africa along with our ancestors, has been intertwined with our species for at least two hundred thousand years. Although the bacterium occupies half the stomachs on earth, its role in our lives was never clear. Then, in 1982, to the astonishment of the medical world, two scientists, Barry Marshall and J. Robin Warren, discovered that H. pylori is the principal cause of gastritis and peptic ulcers; it has since been associated with an increased risk of stomach cancer as well. Until that discovery, for which the men shared a Nobel Prize, in 2005, stress, not an infection, was assumed to be the major cause of peptic ulcers.
Antibiotic usage is the most commonly cited risk factor for hospital-acquired Clostridium difﬁcile infections (CDI). The increased risk is due to disruption of the indigenous microbiome and a subsequent decrease in colonization resistance by the perturbed bacterial community; however, the speciﬁc changes in the microbiome that lead to increased risk are poorly understood.
We developed statistical models that incorporated microbiome data with clinical and demographic data to better un- derstand why individuals develop CDI. The 16S rRNA genes were sequenced from the feces of 338 individuals, including cases, diarrheal controls, and nondiarrheal controls. We modeled CDI and diarrheal status using multiple clinical variables, including
age, antibiotic use, antacid use, and other known risk factors using logit regression. This base model was compared to models that incorporated microbiome data, using diversity metrics, community types, or speciﬁc bacterial populations, to identify characteristics of the microbiome associated with CDI susceptibility or resistance. The addition of microbiome data signiﬁcantly improved our ability to distinguish CDI status when comparing cases or diarrheal controls to nondiarrheal controls. However, only when we assigned samples to community types was it possible to differentiate cases from diarrheal controls. Several bacterial species within the Ruminococcaceae, Lachnospiraceae, Bacteroides, and Porphyromonadaceae were largely absent in cases and highly associated with nondiarrheal controls. The improved discriminatory ability of our microbiome-based models conﬁrms the theory that factors affecting the microbiome inﬂuence CDI.