Figure 2: Aspects of Prevotella and Faecalibacterium at different levels of resolution highlight the complexity of diet-microbe associations, each showing ties with many dietary variables, but varying in direction and strength by country.
Analysis shows bacterial strains and geography are critical for understanding personalized nutrition
The relationship between what we eat and the trillions of bacteria living in our gut has emerged as one of the most promising frontiers in personalized medicine and nutrition. Individual gut microbiomes play a crucial role in determining how our bodies process food, stay healthy and resist disease. As unique as the gut microbiome’s composition is to a specific individual, so are the patterns that exist within one’s cultural context and eating habits, creating a challenge in discovering meaningful connections between the microbes and lifestyles of diverse populations around the world.
A UC San Diego study published in mSystems, entitled, “A three-country analysis of the gut microbiome indicates taxon associations with diet vary by taxon resolution and population,” addresses this challenge by analyzing gut microbiome data from 1,177 individuals across the United States, United Kingdom and Mexico. The research was led by the UC San Diego Center for Microbiome Innovation (CMI), Danone Research and Innovation, and Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán in Mexico. This international partnership enabled the researchers to examine how dietary associations with gut bacteria vary not only between different levels of bacterial classification but also across different populations with distinct dietary patterns and cultural backgrounds.
“Our motivation for this research was to capture a more global perspective on the relationship between the human gut microbiome, diet and health,” noted Lora Khatib, first author and Neuroscience Graduate Program PhD student with the Knight Lab at UC San Diego. “We aimed to better understand both universal and population-specific microbial patterns, especially how taxa may interact with culturally distinct diets and lifestyles in ways that could influence health outcomes.”
The research utilized The Microsetta Initiative platform, incorporating dietary questionnaires from participants in all three countries and advanced metagenomic sequencing techniques to analyze fecal samples. Rather than relying on traditional methods that identify bacteria only at broad genus levels, the researchers used cutting-edge computational tools to resolve bacterial communities down to individual strains. Their analysis focused on two important bacterial genera commonly associated with diet and health, Prevotella and Faecalibacterium. When the researchers examined these bacteria at the genus level, they remarkably found neither correlated strongly with dietary information, nor were they consistent across different populations. However, when they analyzed the same data at the strain level, a more complex pattern emerged.
While many of the Prevotella strains showed dietary correlations, these associations were specific to both the strain and population studied. For example, the same strain may be positively associated with vegetable intake in one population while showing an association with meat intake in another. Conversely, nearly all of the Faecalibacterium strains showed dietary correlations, many of which were consistent across different strains and positively associated with healthy dietary patterns. The study also revealed that geographic location had the strongest effect on overall microbiome composition, as their machine learning algorithms were able to predict a person’s country of origin with strong accuracy, based solely on their gut bacterial profile.
This study underscores the importance of diverse sampling and high-resolution data in microbiome research, and represents a remarkable step toward understanding the complex relationship between individuals’ diet, geographic location and gut microbiomes. The research team plans to expand the analysis to include samples from Japan and Spain to further diversify their dataset, test patterns observed from this three-country study and deepen their understanding of how gut microbiome-diet relationships at the strain level differ across populations with distinct dietary and lifestyle patterns.
Andrew Bartko, corresponding author and executive director of CMI, emphasized, “This work highlights the need for more diverse and representative microbiome research across different populations and supports the development of personalized nutrition strategies. In the future, this kind of research could inform tailored dietary recommendations that take into account both a person’s gut microbiome and their cultural eating patterns, ultimately helping to prevent or manage chronic diseases.”
Additional co-authors include Se Jin Song, Amanda H. Dilmore, Jon G. Sanders, Caitriona Brennan, Alejandra Rios Hernandez, Tyler Myers, Renee Oles, Sawyer Farmer, Charles Cowart, Amanda Birmingham, Edgar A. Diaz, Kat Gilbert, Promi Das, Brent Nowinski, Mackenzie Bryant, Caitlin Tribelhorn, Karenina Sanders-Bodai, Antonio González, Daniel McDonald and Rob Knight of UC San Diego; Oliver Nizet of Johns Hopkins University; Nicole Litwin of Crohn’s and Colitis Foundation, New York; Soline Chaumont, Jan Knol, Guus Roeselers, Manolo Laiola, Sudarshan A. Shetty, Patrick Veiga, Julien Tap, Muriel Derrien, Hana Koutnikova, Aurélie Cotillard and Christophe Lay of Danone Global and Innovation; and Armando R. Tovar, Nimbe Torres and Liliana Arteaga of Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán.
The Center for Microbiome Innovation is proud to include Se Jin Song, Andrew Bartko and Rob Knight on its leadership team.
About the UC San Diego Center for Microbiome Innovation (CMI): CMI inspires and sustains vibrant collaborations between industry leaders and interdisciplinary teams of UC San Diego researchers to fuel discovery and innovation in the world of microbiome science. The Center encompasses a diverse range of expertise in microbiome sampling, -omics technologies and data analysis, using high-performance computing environments, statistical frameworks and AI methodologies. If you are interested in discussing potential partnership opportunities with the CMI team, contact cmiinfo@ucsd.edu for more information.
This work was funded by Danone Nutricia Research and the Center for Microbiome Innovation and supported by The Microsetta Initiative.
