A new study demonstrated that computer models of gut metabolism can predict which probiotics will successfully establish themselves in a person’s gut and how different prebiotics affect production of health-promoting short-chain fatty acids. The findings were published February 19 th in the open-access journal PLOS Biology by Sean Gibbons of the Institute for Systems Biology, US, and colleagues.
Probiotic and prebiotic supplements show highly variable results across individuals, making it difficult to predict who will benefit from these interventions. This variability comes from complex interactions between introduced probiotics, each person’s existing gut microbiota, and their diet.
In the new work, researchers first tested a metabolic model on data from two previous studies in which participants diagnosed with type 2 diabetes were given a placebo or probiotic/prebiotic mixture designed to improve glucose control and healthy participants were given a placebo or a probiotic treatment designed to teat recurrent Clostridioides difficile infections, respectively. The model predicted with 75%-80% accuracy which probiotic species would successfully colonize each person’s gut in the two cohorts. It also revealed associations between the engraftment success of one bacterial strain and people’s blood glucose levels, suggesting a mechanism for treatment efficacy in diabetes.
Then, the team tested the ability of the model to make predictions in a third group of 1,786 generally healthy individuals who were shifting their diets from low- to high-fiber. It was able to predict responses to increasing dietary fiber on both molecules in the gut and on cardiometabolic markers.
“Taken together, these findings demonstrate the utility of [metabolic models] as a predictive framework for assessing prebiotic, probiotic, and dietary interventions at the individual and population levels,” the authors say. “Ultimately, leveraging [metabolic models] in a clinical setting could enable precision microbiome therapeutics, optimizing probiotic, prebiotic, and dietary intake to more effectively treat a wide range of acute and chronic diseases.”
First author Nick Quinn-Bohmann states, “Here, we bridge the gap between probiotic design and real-world application, using deep mechanistic insight to identify the right intervention for each individual.”
Sean Gibbons adds, “This work further demonstrates the potential of microbial community-scale metabolic models (MCMMs) as tools for designing and optimizing personalized probiotic and prebiotic interventions.”
In your coverage, please use this URL to provide access to the freely available paper in PLOS Biology : https://plos.io/4bjWQTc
Citation: Quinn-Bohmann N, Carr AV, Gibbons SM (2026) Metabolic modeling reveals determinants of prebiotic and probiotic treatment efficacy across multiple human intervention trials. PLoS Biol 24(2): e3003638. https://doi.org/10.1371/journal.pbio.3003638
Author countries : United States
Funding: This study was funded, in part, by a research grant from Pendulum (to SMG), the manufacturer of the synbiotic tested in Perraudeau et al. (2020). This work was also supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) under award number R01DK133468, and by a Global Grants for Gut Health Award from Nature Portfolio and Yakult (to SMG). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PLOS Biology
Computational simulation/modeling
Not applicable
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SMG is a paid member of the scientific advisory board for Thorne. This work is unrelated to Thorne, and Thorne had no involvement in the study design, data collection and analysis, decision to publish, or manuscript preparation.