New paper shows how to predict prebiotic responders using metagenomics and machine learning
Although several prebiotic substrates have been consistently shown to provide health benefits in human clinical trials, researchers have often struggled to account for responders and non-responders. Observations of variable responses to prebiotics have led to interest in identifying, a priori, prebiotic responders and non-responders as a basis for personalized nutrition.
In a new study led by Synbiotic Health co-founder Bob Hutkins, shotgun metagenomics and machine learning tools were used to identify microbial gene signatures from adult subjects that could be used to predict prebiotic responders and non-responders.
The results confirmed that in general, bifidobacteria were enriched by the prebiotic substrates XOS, FOS, and inulin, and that individuals also showed variable responses to these substrates. The researchers trained classifiers to predict prebiotic responders, and then demonstrated the utility of the model in a human prebiotic consumption trial.
Overall, the findings from this study highlight how pre-intervention profiling of individual microbiomes can be used in a practical manner to enhance the prebiotic response.
Read the study here.