IN VITRO MODELS FOR ASSESSING THE PREBIOTIC POTENTIAL OF GERODIETETIC CEREAL PRODUCTS FOR OLDER ADULTS
Keywords:
gerodietetics, older adults, gut microbiota, prebiotics, cereal products, in vitro digestion, faecal fermentation, short-chain fatty acids, 16S rRNA sequencing.Abstract
Ageing is accompanied by a loss of gut microbial diversity and a decline in bifidobacteria and butyrate-producing bacteria, which makes fibre-rich cereal products a promising basis for gerodietetic foods. Before such products are tested in older volunteers, their prebiotic potential has to be characterised in the laboratory. The aim of this work was to analyse the in vitro models used to assess the prebiotic potential of cereal products and to propose a stepwise assessment scheme adapted to gerodietetic cereal products for older adults. A narrative analytical review of consensus documents, harmonised protocols and descriptions of in vitro models was carried out. Monoculture screening, batch faecal fermentation and dynamic colon models (SHIME, TIM-2) were compared by inoculum, duration, throughput, cost, control of conditions and the information obtained. The analysis shows that reliable assessment requires simulated upper gastrointestinal digestion before colonic fermentation, faecal microbiota from older donors, and appropriate negative and positive controls. Outcome measures are grouped into microbial composition, metabolic activity, markers of protein fermentation, gas production and pH, and selectivity. A six-stage scheme is proposed that links product composition, simulated digestion, tiered fermentation testing and the planning of a clinical study. In vitro data should be interpreted as evidence of prebiotic potential rather than proof of a health benefit.
References
1. Biagi E., Nylund L., Candela M. et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians // PLoS ONE. – 2010. – Vol. 5, № 5. – e10667.
2. Claesson M.J., Jeffery I.B., Conde S. et al. Gut microbiota composition correlates with diet and health in the elderly // Nature. – 2012. – Vol. 488, № 7410. – P. 178–184.
3. O’Toole P.W., Jeffery I.B. Gut microbiota and aging // Science. – 2015. – Vol. 350, № 6265. – P. 1214–1215.
4. Franceschi C., Garagnani P., Parini P. et al. Inflammaging: a new immune–metabolic viewpoint for age-related diseases // Nature Reviews Endocrinology. – 2018. – Vol. 14, № 10. – P. 576–590.
5. Koh A., De Vadder F., Kovatcheva-Datchary P., Bäckhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites // Cell. – 2016. – Vol. 165, № 6. – P. 1332–1345.
6. Makki K., Deehan E.C., Walter J., Bäckhed F. The impact of dietary fiber on gut microbiota in host health and disease // Cell Host & Microbe. – 2018. – Vol. 23, № 6. – P. 705–715.
7. Hamaker B.R., Tuncil Y.E. A perspective on the complexity of dietary fiber structures and their potential effect on the gut microbiota // Journal of Molecular Biology. – 2014. – Vol. 426, № 23. – P. 3838–3850.
8. Ghosh T.S., Rampelli S., Jeffery I.B. et al. Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries // Gut. – 2020. – Vol. 69, № 7. – P. 1218–1228.
9. Venema K., van den Abbeele P. Experimental models of the gut microbiome // Best Practice & Research Clinical Gastroenterology. – 2013. – Vol. 27, № 1. – P. 115–126.
10. Shani-Levi C., Alvito P., Andrés A. et al. Extending in vitro digestion models to specific human populations: perspectives, practical tools and bio-relevant information // Trends in Food Science & Technology. – 2017. – Vol. 60. – P. 52–63.
11. Gibson G.R., Hutkins R., Sanders M.E. et al. Expert consensus document: The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics // Nature Reviews Gastroenterology & Hepatology. – 2017. – Vol. 14, № 8. – P. 491–502.
12. Minekus M., Alminger M., Alvito P. et al. A standardised static in vitro digestion method suitable for food – an international consensus // Food & Function. – 2014. – Vol. 5, № 6. – P. 1113–1124.
13. Brodkorb A., Egger L., Alminger M. et al. INFOGEST static in vitro simulation of gastrointestinal food digestion // Nature Protocols. – 2019. – Vol. 14, № 4. – P. 991–1014.
14. Pérez-Burillo S., Molino S., Navajas-Porras B. et al. An in vitro batch fermentation protocol for studying the contribution of food to gut microbiota composition and functionality // Nature Protocols. – 2021. – Vol. 16, № 7. – P. 3186–3209.
15. Molly K., Vande Woestyne M., Verstraete W. Development of a 5-step multi-chamber reactor as a simulation of the human intestinal microbial ecosystem // Applied Microbiology and Biotechnology. – 1993. – Vol. 39, № 2. – P. 254–258.
16. Van de Wiele T., Van den Abbeele P., Ossieur W., Possemiers S., Marzorati M. The Simulator of the Human Intestinal Microbial Ecosystem (SHIME®) // The Impact of Food Bioactives on Health: in vitro and ex vivo models / ed. K. Verhoeckx et al. – Cham: Springer, 2015. – P. 305–317.
17. Minekus M., Smeets-Peeters M., Bernalier A. et al. A computer-controlled system to simulate conditions of the large intestine with peristaltic mixing, water absorption and absorption of fermentation products // Applied Microbiology and Biotechnology. – 1999. – Vol. 53, № 1. – P. 108–114.
18. Venema K. The TNO in vitro model of the colon (TIM-2) // The Impact of Food Bioactives on Health: in vitro and ex vivo models / ed. K. Verhoeckx et al. – Cham: Springer, 2015. – P. 293–304.
19. Rémond D., Shahar D.R., Gille D. et al. Understanding the gastrointestinal tract of the elderly to develop dietary solutions that prevent malnutrition // Oncotarget. – 2015. – Vol. 6, № 16. – P. 13858–13898.
20. Topping D.L., Clifton P.M. Short-chain fatty acids and human colonic function: roles of resistant starch and nonstarch polysaccharides // Physiological Reviews. – 2001. – Vol. 81, № 3. – P. 1031–1064.
21. Windey K., De Preter V., Verbeke K. Relevance of protein fermentation to gut health // Molecular Nutrition & Food Research. – 2012. – Vol. 56, № 1. – P. 184–196.
22. Gloor G.B., Macklaim J.M., Pawlowsky-Glahn V., Egozcue J.J. Microbiome datasets are compositional: and this is not optional // Frontiers in Microbiology. – 2017. – Vol. 8. – Article 2224.
