The high prevalence of Clostridioides difficile among nursing home elders associates with a dysbiotic microbiome

Abstract

Clostridioides difficile disproportionally affects the elderly living in nursing homes (NHs). Our objective was to explore the prevalence of C. difficile in NH elders, over time and to determine whether the microbiome or other clinical factors are associated with C. difficile colonization. We collected serial stool samples from NH residents. C. difficile prevalence was determined by quantitative polymerase-chain reaction detection of Toxin genes tcdA and tcdB ; microbiome composition was determined by shotgun metagenomic sequencing. We used mixed-effect random forest modeling machine to determine bacterial taxa whose abundance is associated with C. difficile prevalence while controlling for clinical covariates including demographics, medications, and past medical history. We enrolled 167 NH elders who contributed 506 stool samples. Of the 123 elders providing multiple samples, 30 (24.4%) elders yielded multiple samples in which C. difficile was detected and 78 (46.7%) had at least one C. difficile positive sample. Elders with C. difficile positive samples were characterized by increased abundances of pathogenic or inflammatory-associated bacterial taxa and by lower abundances of taxa with anti-inflammatory or symbiotic properties. Proton pump inhibitor (PPI) use is associated with lower prevalence of C. difficile (Odds Ratio 0.46; 95%CI, 0.22–0.99) and the abundance of bacterial species with known beneficial effects was higher in PPI users and markedly lower in elders with high C. difficile prevalence. C. difficile is prevalent among NH elders and a dysbiotic gut microbiome associates with C. difficile colonization status. Manipulating the gut microbiome may prove to be a key strategy in the reduction of C. difficile in the NH.

Commensal antimicrobial resistance mediates microbiome resilience to antibiotic disruption

“…S3) we fit the model NES s , i ~ f (TTP, X ) i + 1∣ID , where NES is the normalized enrichment score for hallmark pathway (or signature) s in sample i , f is a general nonlinear function (the random forest) applied to TTP and species relative abundances X in sample i as fixed effects, and 1|ID indicates the random effect to account for multiple samples from the same patient. To determine the significance of model-inferred associations, we used permutated importance analysis ( 44 ). For the significant associations obtained from the permutated importance calculations (FDR < 0.1), we then applied univariate linear mixed-effects models to determine the directionality (positive/negative) of the association.…”

EXPLANA: A user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies

“…The  datasets highlighted that performing change analyses can produce unique insights compared to using Original longitudinal data without  calculations. Several studies have applied MERFs for feature selection 41,42 , however, they did not use s, which could lead to a possible loss of valuable insights.…”

The contrasting human gut microbiota in early and late life and implications for host health and disease

“…Likewise, residents of an elderly nursing home in America consuming a typical low-fibre-nursing-home diet had lower levels of butyrate producers and greater abundances of dysbiotic species [184]. A more recent study by the same group revealed that the dysbiotic gut microbiota of elderly nursing home residents -characterised by pro-inflammatory bacteria and reduced anti-inflammatory microbes -associated with C. difficile colonization [185].…”

“…The resulting decrease in microbiota diversity and the elimination of health-associated taxa enable C. difficile to become established in the gut [188]. Yet, as previously mentioned, the dysbiotic microbiota of elderly nursing home residents also allows C. difficile to flourish [185]. CDI in hospitalised elderly individuals was also associated with under-representation of gut microbes with potential protective properties including Bacteroides, Alistipes, Lachnospira, and Barnesiella, and overrepresentation of opportunistic pathogens including Klebsiella, Escherichia/Shigella, Sutterella, Enterococcus, Citrobacter, Veillonella, Proteus, Helicobacter, Morganella, Hafnia, Corynebacterium and Staphylococcus [189].…”