Bacterial and viral respiratory tract microbiota and host characteristics in children with lower respiratory tract infections: a matched case-control study

Abstract

Background: Lower respiratory tract infections (LRTIs) are a leading cause of childhood morbidity and mortality. Potentially pathogenic organisms are present in the respiratory tract in both symptomatic and asymptomatic children, but their presence does not necessarily indicate disease. We aimed to assess the concordance between upper and lower respiratory tract microbiota during LRTIs and the use of nasopharyngeal microbiota to discriminate LRTIs from health.

Methods: First, we did a prospective study of children aged between 4 weeks and 5 years who were admitted to the paediatric intensive care unit (PICU) at Wilhelmina Children's Hospital (Utrecht, Netherlands) for a WHO-defined LRTI requiring mechanical ventilation. We obtained paired nasopharyngeal swabs and deep endotracheal aspirates from these participants (the so-called PICU cohort) between Sept 10, 2013, and Sept 4, 2016. We also did a matched case-control study (1:2) with the same inclusion criteria in children with LRTIs at three Dutch teaching hospitals and in age-matched, sex-matched, and time-matched healthy children recruited from the community. Nasopharyngeal samples were obtained at admission for cases and during home visits for controls. Data for child characteristics were obtained by questionnaires and from pharmacy printouts and medical charts. We used quantitative PCR and 16S rRNA-based sequencing to establish viral and bacterial microbiota profiles, respectively. We did sparse random forest classifier analyses on the bacterial data, viral data, metadata, and the combination of all three datasets to distinguish cases from controls.

Findings: 29 patients were enrolled in the PICU cohort. Intra-individual concordance in terms of viral microbiota profiles (96% agreement [95% CI 93-99]) and bacterial microbiota profiles (58 taxa with a median Pearson's r 0·93 [IQR 0·62-0·99]; p<0·05 for all 58 taxa) was high between nasopharyngeal and endotracheal aspirate samples, supporting the use of nasopharyngeal samples as proxy for lung microbiota during LRTIs. 154 cases and 307 matched controls were prospectively recruited to our case-control cohort. Individually, bacterial microbiota (area under the curve 0·77), viral microbiota (0·70), and child characteristics (0·80) poorly distinguished health from disease. However, a classification model based on combined bacterial and viral microbiota plus child characteristics distinguished children with LRTIs from their matched controls with a high degree of accuracy (area under the curve 0·92).

Interpretation: Our data suggest that the nasopharyngeal microbiota can serve as a valid proxy for lower respiratory tract microbiota in childhood LRTIs, that clinical LRTIs in children result from the interplay between microbiota and host characteristics, rather than a single microorganism, and that microbiota-based diagnostics could improve future diagnostic and treatment protocols.

Funding: Spaarne Gasthuis, University Medical Center Utrecht, and the Netherlands Organization for Scientific Research.

Figure 1

Virus detected by quantitative PCR in cases and matched controls RSV=respiratory syncytial virus. hMPV=human metapneumovirus.

Figure 2

NMDS biplots of individual nasopharyngeal microbiota composition at admission in cases with LRTIs and in matched controls (A) shows the nine bacterial species biomarkers determined by random forest analysis on hierarchical clustering results, whereas (B) shows a posteriori projection of covariates that significantly explained the compositional variation between cases and controls (grey represents significance in univariable analysis, and black significance in multivariable analysis) and the association with age (purple). Ellipses represent the SD for all points within each cohort. Stress=0·269. In (A), operational taxonomic units of bacterial species are referred to by their taxonomical annotations and a rank number (shown in parentheses), which is based on the abundance of each given operational taxonomic unit. For readability, only a selection of the covariates explaining the largest variations between cases and controls are displayed in (B). In (B), the age effect (vertical orientation for younger vs older participants) was roughly perpendicular to the disease–health axis (horizontal orientation), showing that age-related differences in microbiota composition per se are not associated with disease. NMDS=non-metric multidimensional scaling. LRTIs=lower respiratory tract infections. *At time of sampling of the participant, at least one family member was experiencing a respiratory tract infection.

Figure 3

ROC curves for distinguishing disease from health for unstratified and stratified sparse random forest classifying models on the basis of 16S rRNA data, viral presence, and patient characteristics (A), and the disease-discriminatory variables that these models encompass (B–F) The random forest models include all cases (B), pneumonia cases (C), bronchiolitis cases (D), wheezing illness cases (E), and mixed cases (F) versus healthy controls. In (B)–(F), the x-axis shows the importance of the variable to the accuracy of the model, which was estimated by calculating the mean decrease in Gini after randomly permuting the values of each given variable (mean and SD, 100 replicates); the direction of the associations was estimated post hoc with point biserial correlations. Because multiple OTUs of individual bacterial species were identified, we refer to OTUs by their taxonomical annotations and a rank number (shown in parentheses), which is based on the abundance of each given OTU. ROC=receiver operating characteristic. RSV=respiratory syncytial virus. LRTIs=lower respiratory tract infection. OTU=operational taxonomic unit.