(P19-054-25) Development of a Korean Nutrition Model for In Silico Gut Microbiome Analyses Integrated With Nutrigenomics

Objectives: Dietary habits critically shape the gut microbiome's metabolic phenotypes, influencing host health and disease. In silico analyses predict disease-related microbial metabolic features, but accurate nutrition models are difficult to construct due to lack of varying dietary details across populations. We developed a Korean nutrition model using a decision algorithm, confirming that different models yield distinct outcomes in in silico microbiome analyses.

Methods: Based on a decision algorithm designed in this study, the recommended Korean diet from the Ministry of Food and Drug Safety’s nutrition database was incorporated into a Korean nutrition model. Growth rates and metabolic fluxes of gut microbiota in healthy individuals and those with inflammatory bowel disease from Korea and Europe were analyzed using MICOM, and the results were compared.

Results: Existing food databases were largely confined to Western regions, and detailed nutritional information was lacking in Korea. By comparing Korean nutrition data with a European model, gaps in carbohydrates and vitamins were supplemented to construct the Korean nutrition model. In silico analyses using MICOM demonstrated that different nutrition models produce distinct results and applying appropriate nutrition models fitting the cohorts can accurately identify key gut microbial features associated with inflammatory bowel disease compared to healthy controls.

Conclusions: Our nutrigenomics-based construction of a Korean nutrition model and subsequent in silico gut microbiome analyses underscore the importance of accurate, population-specific nutrition models for understanding host-microbiome interactions and identifying key microbial metabolic features linked to health and disease.

Funding Sources: This work was partly supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) of Korea grant (2021M3A9I4021431 to DWL), funded by the Ministry of Science and ICT (MSIT), Republic of Korea. This work was also partly supported by the Bioindustrial Technology Development Program of Korea grant (200118770), funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).