# Metabolic modeling reveals determinants of prebiotic and probiotic treatment efficacy across multiple human intervention trials

## TL;DR

This study shows how metabolic models can predict how well probiotics and prebiotics work in individuals, helping personalize microbiome treatments.

## Contribution

The study introduces microbial community-scale metabolic models to predict probiotic engraftment and short-chain fatty acid production in human trials.

## Key findings

- Metabolic models predicted probiotic engraftment with 75%–80% accuracy in two clinical trial cohorts.
- Higher predicted growth rates of Akkermansia muciniphila were linked to lower glucose levels in one trial.
- Personalized prebiotic selection could enhance probiotic efficacy based on model simulations.

## Abstract

Prebiotic, probiotic, and combined (synbiotic) interventions often show variable outcomes across individuals, driven by complex interactions between introduced biotics, the endogenous microbiota, and the host diet. Predicting individual-specific success or failure of probiotic and prebiotic therapies remains a major challenge. Here, we leverage microbial community-scale metabolic models (MCMMs) to predict probiotic engraftment and microbiota-mediated short-chain fatty acid (SCFA) production in response to probiotic and prebiotic interventions. Using data from two human clinical trial cohorts, testing a five-strain probiotic combined with the prebiotic inulin designed to improve metabolic health and an eight-strain probiotic designed to treat recurrent Clostridioides difficile infections, respectively, we show that MCMM-predicted engraftment largely agrees with measurements, achieving 75%–80% accuracy. Engraftment probabilities varied across taxa. MCMMs captured treatment-driven shifts in predicted SCFA production, and higher model-predicted growth rates of Akkermansia muciniphila were negatively associated with glucose area under the curve (AUC) in the first trial, providing clues about the mechanisms underlying treatment efficacy. Extending these models to a third human cohort undergoing a healthy diet and lifestyle intervention revealed substantial inter-individual variability in predicted responses to increasing dietary fiber, which were significantly associated with baseline-to-follow-up changes in cardiometabolic health markers. Finally, our simulation results suggested that personalized prebiotic selection may further enhance probiotic efficacy. Together, these findings demonstrate the potential of metabolic modeling to guide personalized microbiome-mediated interventions.

Prebiotic and probiotic interventions can induce therapeutically relevant shifts in the human microbiome, but their effects are variable across individuals. This study shows that metabolic models can be used to predict probiotic engraftment and shifts in microbiota-mediated short-chain fatty acid production after synbiotic intervention.