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The human gut microbiota is a complex community of microorganisms, dominantly bacteria, which reside in the human gastrointestinal tract [1]. Emerging evidence suggests that the gut microbial diversity and composition and its relevant metabolites such as short-chain fatty acids (SCFAs) are involved in numerous physiological processes in the human body [2], which could affect metabolic diseases including metabolic syndrome (MetS) [3]. Diet is one of the major contributors to gut microbiota and its metabolite production. However, to date, comprehensive studies on associations of the gut microbiota and its metabolites interacting with diet in human metabolic health in Korean populations have not been sufficiently done. In collaboration with IARC-WHO and NAS-RDA, we examined interactions between the gut microbiota and its relevant metabolites and diet, and its cross-sectional associations with metabolic health among 348 Korean adults. A dietary pattern (DP) predicting the gut microbial diversity derived by reduced rank regression (RRR, a supervised dimension-reducing technique) was characterized by high intakes of vegetables, fermented legumes, nuts & seeds, fish & seafood, and coffee and low intakes of processed meats and other non-alcoholic beverages. The DP score was correlated with fasting glucose level (r=-0.14; p=0.01) negatively and HDL cholesterol level (r=0.15; p=0.006) positively. Also, this DP was inversely associated with metabolically unhealthy status [OR=0.73; 95% CI=0.55-0.98] defined as having 2 or more among 4 metabolic syndrome components (waist circumference, triacylglycerol, HDL-cholesterol, and fasting glucose). In addition, using Using multi-omics factor analysis (MOFA), a computational framework for unsupervised integration and discovery of the principal axes of biological and technical variations in multi-omics datasets [4], a latent factor was identified explaining the maximum variance of the gut microbial composition and plasma SCFAs of 348 Korean adults. In this latent factor, the Blautia microbe and plasma acetic acid were the most important features, which were positively associated with a high risk of metabolic syndrome. Furthermore, using XGBoost and SHAP value, another machine learning technique to predict this latent factor based on 27 food group intakes, we found that fruit vegetables and dairy products were negative factors, whereas starch vegetables were positive factors. The findings of our study suggest that diet-gut microbiota interactions can be associated with the status of metabolic health in Korean adults. In particular, plant- and fermented-food-based diet in Korean adults is associated with favorable gut microbial diversity and composition, which may improve metabolic health.
References
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- Herrema, H. & Niess, J. H. Intestinal microbial metabolites in human metabolism and type 2 diabetes. Diabetologia 63, 2533–2547 (2020).
- Lynch, S. V. & Pedersen, O. The Human Intestinal Microbiome in Health and Disease. N. Engl. J. Med. 375, 2369–2379 (2016).
- Argelaguet, R. et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
| Keywords | Gut Microbiota, Diet, Metabolic disorders, Hyperglycaemia, Dyslipidaemia, Multi-Omics Factor Analysis |
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