Baseline characteristics of participants

Of the 109 participants enrolled, 86 mother-infant pairs, with an additional infant (1 twin set) were included in the final analysis (Fig. 1). Of these, 40% self-administered probiotics, which are presented in Table 1. In total, 52 infants were not exposed to probiotics and 35 were exposed to probiotics during the first 6 months of life either through their mother’s breast milk (n = 17), directly supplemented (n = 6), or both (n = 12). Subcategorizing participants resulted in 13 “low” probiotic consumers and 22 “high” probiotic consumers. The demographic and clinical characteristics between the participants categorized in the probiotics or no probiotics supplementing groups were comparable (Table 2). There were no differences in maternal age, health, or education between the two groups. Our cohort predominantly included Euro-Canadian women with post-secondary education. Likewise, the clinical characteristics of the infants at birth were similar between the two groups. The infants were born 39.8 ± 1.38 standard deviation (SD) weeks of gestation in the no probiotics group and 39.9 ± 1.67 SD weeks in the probiotics group. The birthweights of the infants in these groups were 7.84 ± 1.06 SD lbs in the supplemented group and 7.72 ± 0.90 SD lbs in the non-supplemented group. Similar in both groups, 71% of infants received their recommended immunizations. Six infants were fed probiotics directly resulting in their samples being categorized as the “probiotics” category whereas the mother’s breast milk was categorized as “no probiotics” in our analysis. Demographic and clinical data was not available for one infant because they were adopted. In addition to demographic and clinical characteristics of the participants we collected environmental and socioeconomic factors (Table 3) which may impact infant health, such as presence of household pets, siblings, and daycare attendance. As the original study was on the effects of fish oil supplementation, we also included fish oil as a potential confounding variable. Of the participants that reported infant illnesses, 63% had pets with a roughly equal split between probiotics (30%) and no probiotics (33%) participants; 77% of the participants had siblings and 48% of these siblings, or the infant themselves, were in daycare or preschool during the study (22% no probiotics: 26% probiotics). Using food frequency questionnaires, it was determined that yogurt consumption was comparable between the two groups and was omitted as a potential confounding variable (Table 3). Environmental and socioeconomic data was missing for two participants (1 probiotic and 1 no probiotic participant) and therefore were excluded in the multi-model analysis. Likewise, a meconium and a seven-month sample were only available from the probiotics group and therefore microbial comparisons were not made at these time points.

Figure 1 Flow of participants through the trial. Full size image

Table 1 Probiotics consumed by participants. Full size table

Table 2 Baseline Demographic, and Clinical Characteristics. Full size table

Table 3 Environmental and Socioeconomic Variables. Full size table

Overall microbial richness and evenness are not different between the probiotics and no probiotics infant groups

To understand if probiotic exposure during the first 6 months of life influence the infant microbial ecosystem, we examined their fecal microbiota using 16S rRNA target gene high-throughput sequencing. Alpha diversity, a measure of species richness and evenness within a single sample, was determined using the following indices: Pielou’s Evenness, Shannon’s diversity index, Faith’s phylogenetic diversity (PD), and observed species richness. Pielou’s evenness measures community evenness whereas Shannon’s diversity index is a quantitative measure of community richness. Faith’s PD similarly measures community richness but also incorporates phylogenetic relationships between features. The observed species index, which measures the number of unique features observed within a single sample, was chosen as a proxy to assess the exposure route (direct infant supplementation or via breast milk) using a two-way ANOVA. No difference was detected between the two routes and data was combined for all presented microbial analyses (Table 4). None of the alpha diversity measures were significantly different between the probiotics and no probiotics group at any age; however, increases in microbial richness appeared to occur at different time-points (Fig. 2) in infants not supplemented with probiotics. While there were no changes in Pielou’s evenness, Shannon’s diversity, or observed species richness from one week to 6 months of age in either cohort, Faiths PD showed a significant increase in community richness in the no probiotics group at months 4 (mean = 3.9; 95% confidence interval (CI)[3.4, 4.4], P < 0.05), and 5 (mean = 3.6; 95% CI [3.2, 4.0], P < 0.05) when compared to the first week of life (mean = 1.9; 95% CI [1.5, 2.2], P < 0.05), whereas infants exposed to probiotics did not. Subcategorizing infants into high and low probiotic exposure does not change the results of Faith’s PD, but showed additional increases in observed species richness at months 5 and 6 in the no probiotics group when compared to the first one week of life (Figure S1.A). Overall, these results reveal that infants who are exposed to probiotics have comparable, or lower, microbial diversity to non-supplemented infants.

Table 4 Two factor ANOVA on exposure route using observed species richness. Full size table

Figure 2 Alpha diversity measures of fecal microbiota over a 6-month period of probiotics and no probiotics exposed infants. The alpha diversity in the infants’ stool did not differ between the two groups at any time point (1 week to 6 months of age). While the two groups were not different from each other, Faith’s phylogenetic diversity in the no probiotics group significantly increased at months 4 and 5 when compared to the first week, whereas the probiotics group showed no significant increases. *Denotes P < 0.05. Full size image

Probiotic supplementation is associated with higher Bifidobacterium at one week of age

Several beta diversity measures were used to determine the extent of change in microbial community composition between the groups. Principal coordinate analysis (PCoA), an ordination method which utilizes a distance matrix constructed from non-Euclidean distance measures, was first used to visualize Bray-Curtis dissimilarity between samples (Fig. 3A). This visualization revealed no distinct clustering between the probiotics and no probiotics group at any time point except at one week of age. To further determine, through statistical testing, whether bacterial populations were influenced by probiotic exposure, a permutational multivariate analysis of variance (PERMANOVA) was applied to the Bray-Curtis dissimilarity matrix. The results of the PERMANOVA agreed with the visualization and showed no main difference between the two groups at any age except for at one week of age (P = 0.016) and 5 months (P = 0.037). In addition to the Bray-Curtis dissimilarity matrix, weighted and unweighted UniFrac measures were used to assess phylogenetic distances between sets of taxa21. The unweighted UniFrac is a qualitative measure based on the presence or absence of bacteria whereas the weighted UniFrac considers the relative abundance of the taxa. In this analysis, the unweighted UniFrac showed that the overall composition of the samples’ community in the first 6 months of life were similar across groups (Fig. 3B). As expected, the microbial presence changed over the 6-month period in both the probiotics and no probiotics group; however, these changes were comparable between both groups. Differences in relative abundances of taxa (weighted UniFrac) between the probiotics and no probiotics group were detected at one week of age (P = 0.006) (Fig. 3B). While the clustering did not change (Figure S1B), these results did not persist when subdivided into high and low probiotic exposure (Table S1), likely due to the low sample size in each group (none n = 6; low n = 3; high n = 4).

Figure 3 Beta diversity assessment of microbial communities over a 6-month period of probiotics and no probiotics exposed infants. (A) PCoA plots based on the Bray-Curtis dissimilarity distance show no distinct clustering of microbial communities at any age except for at 1 week. (B) Table summary of PERMANOVA results ran on the Bray-Curtis dissimilarity, and weighted and unweighted UniFrac distances between the two groups. The values at the intersect of a blue cell (probiotics group) and an orange cell (no probiotics group) show the estimated P value of the corresponding time points. Full size image

To determine which taxa were differentially abundant between the two groups, analysis of composition of microbiomes (ANCOM) was performed at each age22. Overall, ANCOM did not identify significant features at any age with the exception of Bifidobacterium spp. being more abundant in the probiotics group at one week of age (Fig. 4A). A linear discriminant analysis of effect size (LEfSe)23 validated these results (Fig. 4B) and with the 8 significantly discriminative features identified before internal Wilcoxon testing, showed a significant (α = 0.05) increase in the abundance of Bifidobacterium longum in the probiotics group as well as an increase in Gammaproteobacteria in the no probiotics group at 1 week of age. The cladogram shows the bacterial distribution in the sample groups and differences in abundances between them, according to LEfSe, were displayed as colors and circle’s diameters. LEfSe identified several other taxonomic differences between the probiotics and no probiotics groups at other ages (Figure S2). However, as the ANCOM results did not find any differences at these ages, we cautiously excluded these results to avoid false positive discovery, though future studies may benefit in using these results as possible a priori genera of interest. These results did not change when the infants directly consuming BioGaia according to the manufacturers recommendations were analyzed separately (4 BioGaia supplemented infants: 6 non-supplemented infants), in an attempt to standardize the brand and dose of probiotics consumed (Fig. 4C). Overall, there is evidence that the community composition and differential taxon abundances are associated with probiotic supplementation in infants at one week of age using the whole cohort (unstandardized) and a small subset of infants consuming the same brand of probiotic.

Figure 4 Differences in the abundance of taxa between the groups were assessed using two complimentary approaches at each time-point. (A) Differences in the relative abundance of Bifidobacterium spp. in the probiotics group at one week of age was detected as significantly different using ANCOM (P < 0.05, F-statistic = 7.5). (B) LEfSe results showing significantly different taxa between fecal samples between probiotics and no probiotics infants at one week of age. The cladogram reports the taxa showing different abundance values according to LEfSe. Colors indicate the lineages that are encoded within corresponding samples. Higher taxa abundance in the probiotics group is colored blue whereas higher taxa abundance in the no probiotics group is colored orange. (C) Infants directly supplemented with BioGaia according to the manufacturers recommendations show similar results to (A and B). P < 0.05. Full size image

Probiotics are not associated with gut microbiome function

The trillions of bacteria present in the gut have redundant genetic contribution and therefore similar functions. Predicted metagenomic functions of the microbial communities using phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt)24, revealed no predicted functional differences between the two cohorts. Another method of assessing the functions of the intestinal microbial communities is through measuring fecal SCFAs, the by products of bacterial fermentation of carbohydrates. We analyzed the presence of fecal acetate, butyrate, and propionate which have been found to play an important role in regulating intestinal homeostatic and immune responses25. Probiotic exposure was not associated with changes in the abundances of these SCFAs found in the infants’ stool showing that despite some taxonomic differences, neither predicted gut microbial function nor SCFAs were different between groups (Fig. 5).

Figure 5 Abundances of fecal SCFAs acetic acid, butyric acid, and propionic acid between probiotics and no probiotics exposed infants at 5 months of age expressed as mass % (g SCFA/g dry weight stool). Full size image

Probiotic supplementation is not associated with breast milk immunity

Innate and adaptive immune cells found in breast milk protect infants from mucosal infections while their immune system develops26. To determine if probiotic supplementation influenced the protective potential of breast milk, immune cytokines were examined in the breast milk. In addition, IgE was analyzed to determine if probiotics were associated with markers of allergies whereas sIgA was analyzed as a marker of mucosal protection against enteric disease27. The multivariate generalized linear models (GLM) of the breast milk cytokines showed no differences between groups (likelihood ratio test = 23.8, adj. P = 0.5) (Fig. 6). Similarly, there were no significant differences between levels of sIgA or IgE in breast milk (Fig. 7), showing that probiotic supplementation was not associated with protective immune responses in breast milk.

Figure 6 Immune markers in breast milk of mothers with or without probiotic supplementation at 5 months. The scatter dot plot shows the mean and standard error mean. Full size image

Figure 7 Infant exposure to probiotics correlates with increased mucosal illness during the first 2 years of life and corresponding immune responses at 5 months of age. (A) Infants exposed to probiotics had significantly higher reported mucosal infections compared to the no probiotics group but were able to clear infections at similar rates. (B) Probiotic exposure during the first 6 months is associated with no changes in breast milk sIgA but modest increases in fecal sIgA. (C) There were no differences in either breast milk or infant stool IgE levels. *Denotes P < 0.05. Full size image

Higher probiotic intake correlates with increased mucosal infections

To determine if probiotics are correlated with infant mucosal health including enteric and respiratory infections, sickness incidence reports were collected over a two-year period. The responses from these reports are presented in Table S2. Oral, respiratory, and gastrointestinal infections were classified as mucosal infections and included in subsequent analysis. There was a 43% (n = 15) response in participants that were self-administering with probiotics and a 27% (n = 14) response in the participants who did not self-administer probiotics. We found that both cohorts were able to clear infections at comparable rates (probiotics: 9.16 ± 1.21; no probiotics: 7.71 ± 1.25); however, infants exposed to probiotics during the study had a higher frequency of mucosal infections reported (5.87 ± 1.23) in the first 2 years of life compared to the no probiotics group (2.71 ± 0.53) (Fig. 7A). Results from a multi-model inference approach confirmed these findings while accounting for covariates. Of the 64 possible models, 5 models within two AICc (corrected Akaike information criteria) of the best model were averaged. The explanatory variables MOD and presence of pets did not appear in any of the top model sets, while siblings and fish oil supplements appeared in 2, preschool in 1, and probiotics appeared in all five sets, highlighting the importance of this variable (Table 5). Model averaging indicated that probiotic supplementation had the strongest, positive effect on the incidences of sickness (estimated coefficient (EC) 3.3, P = 0.025, EC/standard error >2) while presence of a sibling (EC 2.61, P = 0.114), fish oil supplementation (EC −2.15, P = 0.150), and preschool attendance (EC 1.19, P = 0.429) did not have a significant effect and had higher variability (EC/standard error <2).

Table 5 Results of multi-model inference on probiotics supplementation and incidences of mucosal infection. Full size table

As probiotics have been shown to influence the humoral immune responses, we examined sIgA and IgE in the infants’ stool to determine if sickness incidence corresponded with humoral immune markers. Despite probiotics having no effect on the immunoglobulins in the mothers’ breast milk, probiotics were associated with a modest increased sIgA levels measured in infants’ stool at 5 months of age (difference between means = 0.10 ± 0.05; 95% CI [0.001, 0.20], P < 0.05) (Fig. 7B). Similar to the mothers’ breast milk, there was no significant difference in the IgE measured from the infants’ stool (difference between means = −113.1 ± 114.8; 95% CI [−352.6, 126.5], P = 0.16) (Fig. 7C) suggesting no relation with allergy susceptibly.