Short-chain fatty acids (SCFAs) are beneficial molecules produced by gut bacteria, linked to improved metabolism, reduced inflammation, better cardiovascular health, and lower cancer risk. However, SCFA profiles can vary significantly between individuals on the same diet, and current tools cannot predict this variation.

Researchers at the Institute for Systems Biology (ISB) have developed a novel method to simulate personalized, microbiome-mediated responses to diet. They use a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific SCFA production rates in response to different dietary, prebiotic, and probiotic inputs.

Essentially, ISB scientists can create a “digital twin” of gut microbiome metabolism to simulate personalized dietary responses using gut microbiome sequencing data and dietary intake information. Their findings were detailed in a paper published in Nature Microbiology.

“To a first approximation, the gut microbiome is a bioreactor that converts dietary fibers into these SCFAs,” said Dr. Sean Gibbons, ISB associate professor and co-senior author. “Understanding how the ecology of the gut and dietary intake can be quantitatively mapped to SCFA outputs will represent a major advance in translating microbiome science into the clinic.”

Unlike black-box machine learning approaches, MCMMs are transparent and mechanistic, involving tens of thousands of metabolites and enzymes across numerous organisms, providing detailed insights into the specific microbes, dietary components, and metabolic pathways that contribute to SCFA production. Despite this transparency, these models' complexity makes experimental validation challenging.

One approach is to measure SCFA production rates for an entire ecosystem and compare these to model predictions. However, measuring SCFAs in the body is difficult because they are rapidly consumed after creation. To address this, the researchers measured SCFA production rates from in vitro communities of human gut bacterial isolates and from ex vivo stool homogenates from different humans incubated with various dietary fibers.

By isolating microbiota-driven SCFA production from host absorption, ISB scientists demonstrated that MCMM predictions were significantly correlated with measured production rates across various fibers for both butyrate and propionate, two abundant and physiologically important SCFAs.

While in vivo measurements of butyrate and propionate production were not feasible, the authors validated the physiological effects of SCFA production through indirect associations with blood-based health markers. They showed that MCMM predictions could differentiate between individuals in a high-fiber feeding study who had divergent immune responses. Most showed reduced systemic inflammation markers, but a subset showed increased inflammation on a high-fiber diet. According to MCMM predictions, this high-inflammation group had a significantly reduced capacity for producing propionate. Additionally, higher predicted butyrate production was associated with lower LDL cholesterol, lower triglycerides, improved insulin sensitivity, reduced systemic inflammation, and lower blood pressure in over 2,000 individuals.

“The predictive accuracy of MCMMs in vitro, coupled with the significant associations between SCFA predictions and health markers in human cohorts, gives us confidence in the utility of these models for precision nutrition,” said lead author Dr. Nick Quinn-Bohmann, a University of Washington graduate student at ISB who recently defended his dissertation.

After validating MCMM predictions, the authors demonstrated the potential of this approach for designing personalized prebiotic, probiotic, and dietary interventions to optimize SCFA production profiles. They simulated butyrate production rates for two different diets—the standard Austrian diet and a vegan high-fiber diet—across a cohort of over 2,000 individuals from the Pacific West of the US. They found that some individuals showed almost no increase in butyrate production on the high-fiber diet (termed “non-responders”), and some even saw a decrease (termed “regressors”). They then simulated three simple co-interventions to boost butyrate production in these groups: adding the prebiotic fiber inulin, adding the prebiotic fiber pectin, or adding a butyrate-producing probiotic (Faecalibacterium). Results showed that no single intervention was optimal for all individuals; some benefited from adding prebiotic fiber, while others required a butyrate-producing probiotic.

“Together, these results represent an important proof of concept for a novel path forward in microbiome-mediated precision nutrition,” said Dr. Christian Diener, co-senior author and assistant professor at the Medical University of Graz in Austria. “But, of course, there is more work to do to validate the predictive capacity of these models in prospective human trials before they can enter clinical practice.”

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