The antibiotic resistance reservoir of the lung microbiome expands with age

Abstract

Antimicrobial resistant lower respiratory tract infections (LRTI) are an increasing public health threat, and an important cause of global mortality. The lung microbiome influences LRTI susceptibility and represents an important reservoir for exchange of antimicrobial resistance genes (ARGs). Studies of the gut microbiome have found an association between age and increasing antimicrobial resistance gene (ARG) burden, however corollary studies in the lung microbiome remain absent, despite the respiratory tract representing one of the most clinically significant sites for drug resistant infections. We performed a prospective, multicenter observational study of 261 children and 88 adults with acute respiratory failure, ranging in age from 31 days to ≥ 89 years, admitted to intensive care units in the United States. We performed RNA sequencing on tracheal aspirates collected within 72 hours of intubation, and evaluated age-related differences in detectable ARG expression in the lung microbiome as a primary outcome. Secondary outcomes included number and classes of ARGs detected, proportion of patients with an ARG class, and composition of the lung microbiome. Multivariable logistic regression models (adults vs children) or continuous age (years) were adjusted for sex, race/ethnicity, LRTI status, and days from intubation to specimen collection. Detection of ARGs was significantly higher in adults compared with children after adjusting for sex, race/ethnicity, LRTI diagnosis, and days from intubation to specimen collection (adjusted odds ratio (aOR): 2.16, 95% confidence interval (CI): 1.10-4.22). A greater proportion of adults compared with children had beta-lactam ARGs (31% (CI: 21-41%) vs 13% (CI: 10-18%)), aminoglycoside ARGs (20% (CI: 13-30%) vs 2% (CI: 0.6-4%)), and tetracycline ARGs (14% (CI: 7-23%) vs 3% (CI: 1-5%)). Adults ≥70 years old had the highest proportion of these three ARG classes. The total bacterial abundance of the lung microbiome increased with age, and microbiome alpha diversity varied with age. Taxonomic composition of the lung microbiome, measured by Bray Curtis dissimilarity index, differed between adults and children (p = 0.003). The association between age and increased ARG detection remained significant after additionally including lung microbiome total bacterial abundance and alpha diversity in the multivariable logistic regression model (aOR: 2.38, (CI: 1.25-4.54)). Furthermore, this association remained robust when modeling age as a continuous variable (aOR: 1.02, (CI: 1.01-1.03) per year of age). Taken together, our results demonstrate that age is an independent risk factor for ARG detection in the lower respiratory tract microbiome. These data shape our understanding of the lung resistome in critically ill patients across the lifespan, which may have implications for clinical management and global public health.

Keywords: aging; antibiotic resistance; antimicrobial resistance; lung microbiome; metatranscriptomics; resistome.

Figure 1.

(A) Frequency of children (translucent) and adults (solid) with each antimicrobial resistance gene (ARG), stratified by ARG class. (B) Number of ARGs detected in children and adults by age subgroups. Two outliers were omitted for visualization purposes; one 11–18 year-old patient with 18 ARGs detected and another 70–79 year-old patient with 12 ARGs detected. (C) Number of ARG classes detected in children and adults by age subgroups. For Figures B and C, p-values were calculated using Wilcoxon-rank sum test and adjusted for multiple comparisons with False Discovery Rate (FDR) correction. The asterisks indicate statistically significant comparisons; all had a p-value <0.01. (D) Proportion of patients with ARGs by ARG class, stratified by pediatric and adult cohorts. The 95% confidence intervals were calculated by the Clopper-Pearson exact binomial method. P-values were obtained by Pearson’s Chi-square test and Fisher’s exact test for samples with <5 total ARGs. (E) Beta diversity of antimicrobial resistome children and adults. P-value calculated based on the Bray-Curtis dissimilarity index and the PERMANOVA test with 1000 permutations. Abbreviation: TMP-SMX, trimethoprim-sulfamethoxazole; NMDS, nonmetric multidimensional scaling.

Figure 2.

Multivariable logistic regression model evaluating the association of (A) binary age and (B) age subgroups with the presence of ARGs, accounting for sex, race/ethnicity, and lower respiratory tract infection (LRTI) status.

Figure 3.

(A) Bacterial abundance in the lung microbiome measured in total bacterial alignments to the NCBI NT database per million reads sequenced (NT rpm) in children and adults by age subgroups. (B) Alpha diversity, calculated by the Shannon diversity index, of the bacterial lung microbiome of children and adults by age subgroups. (C) Beta diversity of the bacterial lung microbiome of children and adults. P-value calculated based on the Bray-Curtis dissimilarity index and the PERMANOVA test with 1000 permutations. (D) Statistically significant (p-value <0.05) differential abundant bacterial genera, by log₂ fold change of bacterial counts, detected in children and adults. Bar colors indicate whether the species was more abundant in children (blue) or adults (red). (E) Frequency of the bacterial species detected in ≥5% of children (translucent) and adults (solid) among the differentially abundant bacterial genera. For patients with multiple species detected per genus, only the most abundant species was included in this analysis. Abbreviations: NT rpm, sequencing alignments to the NCBI NT database per per million reads sequenced; NMDS, nonmetric multidimensional scaling.

Figure 4.

(A) Multivariable logistic regression model evaluating the association of binary age with the presence of ARGs, accounting for total bacterial abundance (NT rpm) per patient sample, bacterial alpha diversity. (B) Statistically significant (p <0.05) differentially abundant bacterial genera, by log2 fold change of bacterial counts, detected in patients with ARGs compared with patients without ARGs. All detected bacterial genera were more prevalent in patients with ARGs compared with patients without ARGs.