Does entry to center-based childcare affect gut microbial colonization in young infants?

Abstract

Entry to center-based childcare (CC) at three months of life can be an important challenge for infants as it includes major stressors such as long maternal separations and frequently changing caregivers. Stress and the new environment may in turn alter the composition of the gut microbiota with possible implications for future health outcomes. As part of an ongoing longitudinal study, we investigated whether CC, as compared to being cared for by the parents at home, alters the composition of the gut microbiota, while accounting for known covariates of the infant gut microbiota. Stool samples of infants who entered CC (n = 49) and control infants (n = 49) were obtained before and four weeks after CC entrance. Using Redundancy analysis, Random Forests and Bayesian linear models we found that infant gut microbiota was not affected in a uniform way by entry to CC. In line with the literature, breastfeeding, birth mode, age, and the presence of siblings were shown to significantly impact the microbial composition.

Figure 1

Infant microbiota composition and dynamics. (A,B) Relative abundance of major genus-like groups and their Phyla with mean abundance of >1% at the population level. (C) Coefficient of Variation (CoV) of all genus-like groups. The infant microbiota is highly variable as it is dominated by only a few taxa, which are also among the most variable. GREEN – Actinobacteria; BLUE – Firmicutes, Class Bacilli; BLACK – Firmicutes excluding Class Bacilli; RED – Bacteroidetes; ORANGE – Proteobacteria.

Figure 2

The impact of birth mode, breastfeeding, siblings and age on the infant gut microbiota. (A) Redundancy analysis (RDA) visualizing microbiota composition of all fecal samples (n = 196) colored by the number of breast-feedings and the size of the points scaled by age in days. Individuals born by C-section are represented as triangles. RDA displays and explains the variation explained in the microbiota, constrained by the predictor variables. Blue arrows depict the significant environmental variables and grey arrows the abundance of bacterial groups. Length of the arrows is a measure of fit. The longer the arrow the higher the association. (B) Venn diagram visualizing the partitioning of the variation explained by the significant predictors. ***P < 0.001, **P < 0.01, *P < 0.05.

Figure 3

Bayesian hierarchical robust linear model posterior distributions for individual bacterial group differences. (A) within group effects, (B) between group effects and (C) C-section and siblings (D) age and breastfeeding. For the covariates (D) the x-axis refers to the magnitude of the slopes whereas for the group comparisons (A–C) it refers to the difference in the means. Taxa are shown in red when the probability that the absolute effect size is >0 exceeds 0.95, given our model and the data.

Figure 4

Bayesian hierarchical robust linear model group posteriors for microbiota alpha diversity. Means of Shannon diversity per group with 95% CI are shown with the black point range. The observed values are shown as black points within the blue bar. The Bayesian posterior predictive intervals are shown in blue. The predictions were made using the median for average number of breast-feedings and median age at PRE and POST.