Back

Administration of Bifidobacterium animalis subsp. lactis Strain BB-12 (R) in Healthy Children: Characterization, Functional Composition, and Metabolism of the Gut Microbiome

Vizioli, C.; Jaime-Lara, R. B.; Daniel, S. G.; Franks, A.; Diallo, A. F.; Bittinger, K.; Tan, T. P.; Merenstein, D. J.; Brooks, B.; Joseph, P. V.; Maki, K.

2023-02-06 genetic and genomic medicine
10.1101/2023.02.02.23285145 medRxiv
Show abstract

The consumption of probiotics may influence childrens gut microbiome and metabolome, which may reflect shifts in gut microbial diversity composition and metabolism. These potential changes might have a beneficial impact on health. However, there is a lack of evidence investigating the effect of probiotics on the gut microbiome and metabolome of children. We aimed to examine the potential impact of a two (Streptococcus thermophilus and Lactobacillus delbrueckii; S2) vs. three (S2 + Bifidobacterium animalis subsp. lactis strain BB-12) strain-supplemented yogurt. Included in this study were 59 participants, aged one to five years old, recruited to phase I of a double-blinded, randomized controlled trial. Fecal samples were collected at baseline, after the intervention, and at twenty days post-intervention discontinuation, and untargeted metabolomics and shotgun metagenomics were performed. Shotgun metagenomics and metabolomic analyses showed no global changes in either intervention groups gut microbiome alpha or beta diversity indices. The relative abundance of the two and three intervention bacteria increased in the S2 and S2 + BB12 groups, respectively, from Day 0 to Day 10. In the S2+BB12 group, the abundance of several fecal metabolites was reduced at Day 10, including alanine, glycine, lysine, phenylalanine, serine, and valine. These fecal metabolite changes did not occur in the S2 group. Future research using longer probiotic intervention durations and in children at risk for gastrointestinal disorders may elucidate if functional metabolite changes confer a protective gastrointestinal effect.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.