Dietary Polysaccharides and Host–Microbiota Interactions: Emerging Strategies for Precision Nutrition in Metabolic Disorders
Keywords:
dietary polysaccharides, gut microbiota, precision nutrition, metabolic disorders, systems architecture, artificial intelligence, data governance, food infrastructureAbstract
The escalating global burden of metabolic disorders demands a fundamental rethinking of nutritional intervention design, moving beyond population-level guidelines toward precision frameworks that account for inter-individual variability in host–microbiota ecosystems. Dietary polysaccharides, as primary substrates for gut microbial fermentation, represent a class of inputs with profound but incompletely leveraged potential to modulate metabolic health. This paper reframes the challenge of polysaccharide-based precision nutrition as a large-scale, multiscale systems problem, where the gut microbiome functions as a distributed biological processor transforming complex carbohydrate structures into systemically active metabolites. We examine the structural trade-offs inherent in designing polysaccharide interventions, from molecular diversity and fermentation kinetics to host metabolic responses, and propose a layered architecture for precision nutrition platforms that integrates multi-omics data ingestion, machine-learning-driven personalization engines, and continuous monitoring loops. Governance challenges, including data sovereignty, algorithmic fairness across diverse populations, and the robustness of microbial ecosystem interventions to perturbation, are dissected in the context of socio-technical infrastructure. Further, we explore the sustainability implications of scaling novel polysaccharide sourcing, from underutilized plant biomass to precision-fermented oligosaccharides, and discuss the policy frameworks needed to translate emerging science into equitable, resilient food systems. By treating the host–microbiota–polysaccharide nexus as an engineerable, governed system, this work provides a blueprint for deploying precision nutrition at scale while safeguarding against unintended systemic failures.
References
1. Sonnenburg, E. D., & Sonnenburg, J. L. (2019). The ancestral and industrialized gut microbiota and implications for human health. Nature Reviews Microbiology, 17(6), 383–390.
2. Cockburn, D. W., & Koropatkin, N. M. (2016). Polysaccharide degradation by the intestinal microbiota and its influence on human health and disease. Journal of Molecular Biology, 428(16), 3230–3252.
3. Coyte, K. Z., Schluter, J., & Foster, K. R. (2015). The ecology of the microbiome: Networks, competition, and stability. Science, 350(6261), 663–666.
4. Magnúsdóttir, S., & Thiele, I. (2018). Modeling metabolism of the human gut microbiome. Current Opinion in Biotechnology, 51, 90–96.
5. Koh, A., De Vadder, F., Kovatcheva-Datchary, P., & Bäckhed, F. (2016). From dietary fiber to host physiology: Short-chain fatty acids as key bacterial metabolites. Cell, 165(6), 1332–1345.
6. Deehan, E. C., Yang, C., Perez-Muñoz, M. E., Nguyen, N. K., Cheng, C. C., Triador, L., ... & Walter, J. (2020). Precision microbiome modulation with discrete dietary fiber structures directs short-chain fatty acid production. Cell Host & Microbe, 27(3), 389–404.
7. Ze, X., Duncan, S. H., Louis, P., & Flint, H. J. (2012). Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. ISME Journal, 6(8), 1535–1543.
8. Baxter, N. T., Schmidt, A. W., Venkataraman, A., Kim, K. S., Waldron, C., & Schmidt, T. M. (2019). Dynamics of human gut microbiota and short-chain fatty acids in response to dietary interventions with three fermentable fibers. mBio, 10(1), e02566-18.
9. Salonen, A., Lahti, L., Salojärvi, J., Holtrop, G., Korpela, K., Duncan, S. H., ... & de Vos, W. M. (2014). Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME Journal, 8(11), 2218–2230.
10. McRorie, J. W., & McKeown, N. M. (2017). Understanding the physics of functional fibers in the gastrointestinal tract: An evidence-based approach to resolving enduring misconceptions about insoluble and soluble fiber. Journal of the Academy of Nutrition and Dietetics, 117(2), 251–264.
11. Gao, Z., Yin, J., Zhang, J., Ward, R. E., Martin, R. J., Lefevre, M., ... & Ye, J. (2009). Butyrate improves insulin sensitivity and increases energy expenditure in mice. Diabetes, 58(7), 1509–1517.
12. Tolhurst, G., Heffron, H., Lam, Y. S., Parker, H. E., Habib, A. M., Diakogiannaki, E., ... & Gribble, F. M. (2012). Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes, 61(2), 364–371.
13. Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., ... & Segal, E. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079–1094.
14. Berry, S. E., Valdes, A. M., Drew, D. A., Asnicar, F., Mazidi, M., Wolf, J., ... & Spector, T. D. (2020). Human postprandial responses to food and potential for precision nutrition. Nature Medicine, 26(6), 964–973.
15. Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017). Shotgun metagenomics, from sampling to analysis. Nature Biotechnology, 35(9), 833–844.
16. Zhao K, et al. Phyllostachys nigra (Lodd ex Lindl) derived polysaccharide with enhanced glycolipid metabolism regulation and mice gut microbiome. Int J Biol Macromol. 2024;257:128588.
17. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
18. Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 119.
19. Stern, A. D., Brönneke, J., Debatin, J. F., Hagen, J., Matthies, H., Patel, S., ... & Goldsack, J. C. (2022). Advancing digital health applications: Priorities for innovation in real-world evidence generation. The Lancet Digital Health, 4(3), e200–e206.
20. Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2020, 191–200.
21. Popejoy, A. B., & Fullerton, S. M. (2016). Genomics is failing on diversity. Nature, 538(7624), 161–164.
22. Zmora, N., Zeevi, D., Korem, T., Segal, E., & Elinav, E. (2016). Taking it personally: Personalized utilization of the human microbiome in health and disease. Cell Host & Microbe, 19(1), 12–20.
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