Antibiotics, pediatric dysbiosis, and disease

Abstract

Antibiotics are by far the most common medications prescribed for children. Recent epidemiological data suggests an association between early antibiotic use and disease phenotypes in adulthood. Antibiotic use during infancy induces imbalances in gut microbiota, called dysbiosis. The gut microbiome's responses to antibiotics and its potential link to disease development are especially complex to study in the changing infant gut. Here, we synthesize current knowledge linking antibiotics, dysbiosis, and disease and propose a framework for studying antibiotic-related dysbiosis in children. We recommend future studies into the microbiome-mediated effects of antibiotics focused on four types of dysbiosis: loss of keystone taxa, loss of diversity, shifts in metabolic capacity, and blooms of pathogens. Establishment of a large and diverse baseline cohort to define healthy infant microbiome development is essential to advancing diagnosis, interpretation, and eventual treatment of pediatric dysbiosis. This approach will also help provide evidence-based recommendations for antibiotic usage in infancy.

Figure 1. Framework for Host-Microbiome Development in Health, Dysbiosis, and Disease

Disease classes are associated with cascading dysbiosis types, with important dependencies on the course of host-microbiome development. Note that disease classes and dysbiosis types are not necessarily mutually exclusive. The proposed mechanisms presented are supported by extensive evidence in the literature, both from mechanistic studies and from epidemiological surveys. Due to the very large number of references, the citations represented in this figure can be found in Table 1.

Figure 2. Trajectories for Infant Recovery after Antibiotic Exposure

(A) Infant gut microbiomes develop rapidly and experience large changes during infancy before becoming indistinguishable from adult microbiomes by age 2. Dysbiosis in infants can displace (no recovery) or delay (slow recovery) development on the normal growth trajectory. (B) Samples were obtained from a single infant over time (Koenig et al., 2011;; and microbiome distance (Bray-Curtis) to self at 2 years old was plotted over time. Fecal samples collected immediately after antibiotics are denoted in blue. A smoothing spline (in light blue) reveals a noticeable change in trajectory of development after use of antibiotics, mirroring the deviation in trajectory predicted in Figure 2A.

Figure 3. Percent Decrease in Gut Microbiome Biodiversity across Studies with Different Antibiotic Exposures

All fecal samples were collected 1 week after antibiotic course was completed, except where noted by subscripts. The Dethlefsen and Relman (2011) study included three subjects (A, B, and C) who received two courses 6 months apart. ^DSample taken during antibiotic treatment; ⁴sample taken 4 weeks after antibiotic completion; ⁸sample taken 8 weeks after antibiotic completion (Dethlefsen et al., 2008; Fouhy et al., 2012; Robinson and Young, 2010; Russell et al., 2012; Tanaka et al., 2009).

Figure 4. Predicted Microbiome Maturity Index

The predictive microbiome maturity index (MMI) for a given child is compared to the true age of that child. The MMI was predicting using random forests regression algorithm trained on the microbiome compositions and true ages of all children except for one being predicted. True age was predicted to within ± 1.3 months (SD of the predicted error), demonstrating the feasibility of modeling the maturation of the gut microbiota as a predictable process across individuals. Microbiome samples were obtained from children living in the US (Yatsunenko et al., 2012).