1932

Abstract

Inadequate dietary fiber consumption has become common across industrialized nations, accompanied by changes in gut microbial composition and a dramatic increase in chronic metabolic diseases. The human gut microbiome harbors genes that are required for the digestion of fiber, resulting in the production of end products that mediate gastrointestinal and systemic benefits to the host. Thus, the use of fiber interventions has attracted increasing interest as a strategy to modulate the gut microbiome and improve human health. However, considerable interindividual differences in gut microbial composition have resulted in variable responses toward fiber interventions. This variability has led to observed nonresponder individuals and highlights the need for personalized approaches to effectively redirect the gut ecosystem. In this review, we summarize strategies used to address the responder and nonresponder phenomenon in dietary fiber interventions and propose a targeted approach to identify predictive features based on knowledge of fiber metabolism and machine learning approaches.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-food-060721-015516
2023-03-27
2024-05-05
Loading full text...

Full text loading...

/deliver/fulltext/food/14/1/annurev-food-060721-015516.html?itemId=/content/journals/10.1146/annurev-food-060721-015516&mimeType=html&fmt=ahah

Literature Cited

  1. Ambrogi V, Bottacini F, O'Sullivan J, O'Connell-Motherway M, Linqiu C et al. 2019. Characterization of GH2 and GH42 β-galactosidases derived from bifidobacterial infant isolates. AMB Express 9:9
    [Google Scholar]
  2. Anderson KL, Salyers AA. 1989. Biochemical evidence that starch breakdown by Bacteroides thetaiotaomicron involves outer membrane starch-binding sites and periplasmic starch-degrading enzymes. J. Bacteriol. 171:3192–98
    [Google Scholar]
  3. Arboleya S, Bottacini F, O'Connell-Motherway M, Ryan CA, Ross RP et al. 2018. Gene–trait matching across the Bifidobacterium longum pan-genome reveals considerable diversity in carbohydrate catabolism among human infant strains. BMC Genom. 19:33
    [Google Scholar]
  4. Armour CR, Nayfach S, Pollard KS, Sharpton TJ. 2019. A metagenomic meta-analysis reveals functional signatures of health and disease in the human gut microbiome. mSystems 4:e00332
    [Google Scholar]
  5. Armstrong H, Mander I, Zhang Z, Armstrong D, Wine E 2021. Not all fibers are born equal: variable response to dietary fiber subtypes in IBD. Front. Pediatr. 8:924
    [Google Scholar]
  6. Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T et al. 2011. Enterotypes of the human gut microbiome. Nature 473:175–80
    [Google Scholar]
  7. Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G et al. 2021. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat. Med. 27:321–32
    [Google Scholar]
  8. Azcarate-Peril MA, Ritter AJ, Savaiano D, Monteagudo-Mera A, Anderson C et al. 2017. Impact of short-chain galactooligosaccharides on the gut microbiome of lactose-intolerant individuals. PNAS 114:E367–75
    [Google Scholar]
  9. Bai J, Li Y, Li T, Zhang W, Fan M et al. 2021. Comparison of different soluble dietary fibers during the in vitro fermentation process. J. Agric. Food Chem. 69:7446–57
    [Google Scholar]
  10. Bang S, Yoo DA, Kim SJ, Jhang S, Cho S, Kim H. 2019. Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data. Sci. Rep. 9:10189
    [Google Scholar]
  11. Barber TM, Kabisch S, Pfeiffer AFH, Weickert MO. 2020. The health benefits of dietary fibre. Nutrients 12:3209
    [Google Scholar]
  12. Benson AK, Kelly SA, Legge R, Ma F, Low SJ et al. 2010. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. PNAS 107:18933–38
    [Google Scholar]
  13. Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M et al. 2020. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26:964–73
    [Google Scholar]
  14. Beukema M, Faas MM, de Vos P. 2020. The effects of different dietary fiber pectin structures on the gastrointestinal immune barrier: impact via gut microbiota and direct effects on immune cells. Exp. Mol. Med. 52:1364–76
    [Google Scholar]
  15. Bhattacharya T, Ghosh TS, Mande SS. 2015. Global profiling of carbohydrate active enzymes in human gut microbiome. PLOS ONE 10:e0142038
    [Google Scholar]
  16. Boger MCL, van Bueren AL, Dijkhuizen L. 2018. Cross-feeding among probiotic bacterial strains on prebiotic inulin involves the extracellular exo-inulinase of Lactobacillus paracasei strain W20. Appl. Environ. Microbiol. 84:e01539
    [Google Scholar]
  17. Cantarel BL, Lombard V, Henrissat B. 2012. Complex carbohydrate utilization by the healthy human microbiome. PLOS ONE 7:e28742
    [Google Scholar]
  18. Cantu-Jungles TM, Bulut N, Chambry E, Ruthes A, Iacomini M et al. 2021. Dietary fiber hierarchical specificity: the missing link for predictable and strong shifts in gut bacterial communities. mBio 12:e01028
    [Google Scholar]
  19. Cantu-Jungles TM, Hamaker BR 2020. New view on dietary fiber selection for predictable shifts in gut microbiota. mBio 11:e02179
    [Google Scholar]
  20. Carrieri AP, Rowe WPM, Winn M, Pyzer-Knapp EO. 2019. A fast machine learning workflow for rapid phenotype prediction from whole shotgun metagenomes. Proceedings of the 33rd AAAI Conference on Artificial Intelligence9434–39. Palo Alto, CA: AAAI
    [Google Scholar]
  21. Castell-Miller CV, Gutierrez-Gonzalez JJ, Tu ZJ, Bushley KE, Hainaut M et al. 2016. Genome assembly of the fungus Cochliobolus miyabeanus, and transcriptome analysis during early stages of infection on American wildrice (Zizania palustris L.). PLOS ONE 11:e0154122
    [Google Scholar]
  22. Cecchini DA, Laville E, Laguerre S, Robe P, Leclerc M et al. 2013. Functional metagenomics reveals novel pathways of prebiotic breakdown by human gut bacteria. PLOS ONE 8:e72766
    [Google Scholar]
  23. Chang CJ, Lin TL, Tsai YL, Wu TR, Lai WF et al. 2019. Next generation probiotics in disease amelioration. J. Food Drug Anal. 27:615–22
    [Google Scholar]
  24. Chen T, Long W, Zhang C, Liu S, Zhao L, Hamaker BR. 2017. Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota. Sci. Rep. 7:2594
    [Google Scholar]
  25. Christensen L, Roager HM, Astrup A, Hjorth MF. 2018. Microbial enterotypes in personalized nutrition and obesity management. Am. J. Clin. Nutr. 108:645–51
    [Google Scholar]
  26. Chung WSF, Walker AW, Louis P, Parkhill J, Vermeiren J et al. 2016. Modulation of the human gut microbiota by dietary fibres occurs at the species level. BMC Biol. 14:3
    [Google Scholar]
  27. Coker JK, Moyne O, Rodionov DA, Zengler K. 2021. Carbohydrates great and small, from dietary fiber to sialic acids: how glycans influence the gut microbiome and affect human health. Gut Microbes 13:e1869502
    [Google Scholar]
  28. Costea PI, Coelho LP, Sunagawa S, Munch R, Huerta-Cepas J et al. 2017. Subspecies in the global human gut microbiome. Mol. Syst. Biol. 13:960
    [Google Scholar]
  29. Cotillard A, Cartier-Meheust A, Litwin NS, Chaumont S, Saccareau M et al. 2022. A posteriori dietary patterns better explain variations of the gut microbiome than individual markers in the American Gut Project. Am. J. Clin. Nutr. 115:432–43
    [Google Scholar]
  30. Creswell R, Tan J, Leff JW, Brooks B, Mahowald MA et al. 2020. High-resolution temporal profiling of the human gut microbiome reveals consistent and cascading alterations in response to dietary glycans. Genome Med. 12:59
    [Google Scholar]
  31. Cummings JH, Engineer A. 2018. Denis Burkitt and the origins of the dietary fibre hypothesis. Nutr. Res. Rev. 31:1–15
    [Google Scholar]
  32. David LA, Materna AC, Friedman J, Campos-Baptista MI, Blackburn MC et al. 2015. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15:R89
    [Google Scholar]
  33. Davis L, Martínez I, Walter J, Goin C, Hutkins RW. 2011. Barcoded pyrosequencing reveals that consumption of galactooligosaccharides results in a highly specific bifidogenic response in humans. PLOS ONE 6:e25200
    [Google Scholar]
  34. Davis L, Martínez I, Walter J, Hutkins R. 2010. A dose dependent impact of prebiotic galactooligosaccharides on the intestinal microbiota of healthy adults. Int. J. Food Microbiol. 144:285–92
    [Google Scholar]
  35. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB et al. 2010. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. PNAS 107:14691–96
    [Google Scholar]
  36. De Filippo C, Di Paola M, Ramazzotti M, Albanese D, Pieraccini G et al. 2017. Diet, environments, and gut microbiota: a preliminary investigation in children living in rural and urban Burkina Faso and Italy. Front. Microbiol. 8:1979
    [Google Scholar]
  37. De Vuyst L, Leroy F 2011. Cross-feeding between bifidobacteria and butyrate-producing colon bacteria explains bifdobacterial competitiveness, butyrate production, and gas production. Int. J. Food Microbiol. 149:73–80
    [Google Scholar]
  38. Deehan EC, Yang C, Perez-Muñoz ME, Nguyen NK, Cheng CC et al. 2020. Precision microbiome modulation with discrete dietary fiber structures directs short-chain fatty acid production. Cell Host Microbe 27:389–404.e6
    [Google Scholar]
  39. Delannoy-Bruno O, Desai C, Castillo JJ, Couture G, Barve RA et al. 2022. An approach for evaluating the effects of dietary fiber polysaccharides on the human gut microbiome and plasma proteome. PNAS 119:e2123411119
    [Google Scholar]
  40. Delannoy-Bruno O, Desai C, Raman AS, Chen RY, Hibberd MC et al. 2021. Evaluating microbiome-directed fibre snacks in gnotobiotic mice and humans. Nature 595:91–95
    [Google Scholar]
  41. Delzenne NM, Rodriguez J 2022. Nutrition and microbiome. Handbook of Experimental Pharmacology: From Obesity to Diabetes J Eckel, K Clément 57–73. Berlin: Springer
    [Google Scholar]
  42. Desai MS, Seekatz AM, Koropatkin NM, Kamada N, Hickey CA et al. 2016. A dietary fiber–deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167:1339–53.e21
    [Google Scholar]
  43. Deschasaux M, Huybrechts I, Murphy N, Julia C, Hercberg S et al. 2018. Nutritional quality of food as represented by the FSAm-NPS nutrient profiling system underlying the Nutri-Score label and cancer risk in Europe: results from the EPIC prospective cohort study. PLOS Med. 15:e1002651
    [Google Scholar]
  44. Díaz R, Torres-Miranda A, Orellana G, Garrido D. 2021. Comparative genomic analysis of novel Bifidobacterium longum subsp. longum strains reveals functional divergence in the human gut microbiota. Microorganisms 9:1906
    [Google Scholar]
  45. Dong TS, Gupta A. 2019. Influence of early life, diet, and the environment on the microbiome. Clin. Gastroenterol. Hepatol. 17:231–42
    [Google Scholar]
  46. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR et al. 2020. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38:685–88
    [Google Scholar]
  47. Duar RM, Casaburi G, Mitchell RD, Scofield LNC, Ramirez CAO et al. 2020. Comparative genome analysis of Bifidobacterium longum subsp. infantis strains reveals variation in human milk oligosaccharide utilization genes among commercial probiotics. Nutrients 12:3247
    [Google Scholar]
  48. El Kaoutari A, Armougom F, Gordon JI, Raoult D, Henrissat B. 2013. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat. Rev. Microbiol. 11:497–504
    [Google Scholar]
  49. Elison E, Vigsnaes LK, Krogsgaard LR, Rasmussen J, Sørensen N et al. 2016. Oral supplementation of healthy adults with 2′-O-fucosyllactose and lacto-N-neotetraose is well tolerated and shifts the intestinal microbiota. Br. J. Nutr. 116:1356–68
    [Google Scholar]
  50. Falony G, Lazidou K, Verschaeren A, Weckx S, Maes D, De Vuyst L. 2009. In vitro kinetic analysis of fermentation of prebiotic inulin-type fructans by Bifidobacterium species reveals four different phenotypes. Appl. Environ. Microbiol. 75:454–61
    [Google Scholar]
  51. Fan Y, Pedersen O. 2020. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19:55–71
    [Google Scholar]
  52. FDA (US Food Drug Adm.) 2016. Food labeling: revision of the nutrition and supplement facts labels. 81 Fed. Reg. 33,741 (May 27) (codified at 21 C.F.R. 101)
  53. Ferrari P, Rinaldi S, Jenab M, Lukanova A, Olsen A et al. 2013. Dietary fiber intake and risk of hormonal receptor-defined breast cancer in the European Prospective Investigation into Cancer and Nutrition study. Am. J. Clin. Nutr. 97:344–53
    [Google Scholar]
  54. Fitzgerald CB, Shkoporov AN, Sutton TDS, Chaplin AV, Velayudhan V et al. 2018. Comparative analysis of Faecalibacterium prausnitzii genomes shows a high level of genome plasticity and warrants separation into new species-level taxa. BMC Genom. 19:931
    [Google Scholar]
  55. Flint HJ. 2020. How gut micro-organisms make use of available carbohydrates. Why Gut Microbes Matter81–96. Cham, Switz.: Springer
    [Google Scholar]
  56. Flint HJ, Scott KP, Duncan SH, Louis P, Forano E. 2012. Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3:289–306
    [Google Scholar]
  57. Fuhren J, Rösch C, ten Napel M, Schols HA, Kleerebezem M. 2020. Synbiotic matchmaking in Lactobacillus plantarum: substrate screening and gene–trait matching to characterize strain-specific carbohydrate utilization. Appl. Environ. Microbiol. 86:e01081–20
    [Google Scholar]
  58. Gaulke CA, Sharpton TJ. 2018. The influence of ethnicity and geography on human gut microbiome composition. Nat. Med. 24:1495–96
    [Google Scholar]
  59. Ghaffari P, Shoaie S, Nielsen LK. 2022. Irritable bowel syndrome and microbiome: switching from conventional diagnosis and therapies to personalized interventions. J. Transl. Med. 20:173
    [Google Scholar]
  60. Gibbons SM, Gurry T, Lampe JL, Chakrabarti A, Dam V. 2022. Perspective: leveraging the gut microbiota to predict personalized responses to dietary, prebiotic, and probiotic interventions. Adv. Nutr. 13:1450–61
    [Google Scholar]
  61. Gill SK, Rossi M, Bajka B, Whelan K. 2020. Dietary fibre in gastrointestinal health and disease. Nat. Rev. Gastroenterol. Hepatol. 18:101–16
    [Google Scholar]
  62. Goh YJ, Klaenhammer TR. 2015. Genetic mechanisms of prebiotic oligosaccharide metabolism in probiotic microbes. Annu. Rev. Food Sci. Technol. 6:137–56
    [Google Scholar]
  63. Griffin NW, Ahern PP, Cheng J, Heath AC, Ilkayeva O et al. 2017. Prior dietary practices and connections to a human gut microbial metacommunity alter responses to diet interventions. Cell Host Microbe 21:84–96
    [Google Scholar]
  64. Groussin M, Poyet M, Sistiaga A, Kearney SM, Moniz K et al. 2021. Elevated rates of horizontal gene transfer in the industrialized human microbiome. Cell 184:2053–67
    [Google Scholar]
  65. Gupta VK, Kim M, Bakshi U, Cunningham KY, Davis JM et al. 2020. A predictive index for health status using species-level gut microbiome profiling. Nat. Commun. 11:4635
    [Google Scholar]
  66. Gurry T, Gibbons SM, Nguyen LTT, Kearney SM, Ananthakrishnan A et al. 2018. Predictability and persistence of prebiotic dietary supplementation in a healthy human cohort. Sci. Rep. 8:12699
    [Google Scholar]
  67. Gurry T, Nguyen LTT, Yu X, Alm EJ 2021. Functional heterogeneity in the fermentation capabilities of the healthy human gut microbiota. PLOS ONE 16:e0254004
    [Google Scholar]
  68. Hamaker BR, Tuncil YE. 2014. A perspective on the complexity of dietary fiber structures and their potential effect on the gut microbiota. J. Mol. Biol. 426:3838–50
    [Google Scholar]
  69. Han W, Ye Y. 2019. A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data. Pac. Symp. Biocomput. 24:236–47
    [Google Scholar]
  70. Harvie R, Chanyi RM, Burton JP, Schultz M. 2017. Using the human gastrointestinal microbiome to personalize nutrition advice: Are registered dietitian nutritionists ready for the opportunities and challenges?. J. Acad. Nutr. Diet. 117:1865–69
    [Google Scholar]
  71. Healey G, Murphy R, Butts C, Brough L, Whelan K, Coad J. 2018. Habitual dietary fibre intake influences gut microbiota response to an inulin-type fructan prebiotic: a randomised, double-blind, placebo-controlled, cross-over, human intervention study. Br. J. Nutr. 119:176–89
    [Google Scholar]
  72. Hitch TCA, Hall LJ, Walsh SK, Leventhal GE, Slack E et al. 2022. Microbiome-based interventions to modulate gut ecology and the immune system. Mucosal Immunol. 15:1095–113
    [Google Scholar]
  73. Hjorth MF, Blædel T, Bendtsen LQ, Lorenzen JK, Holm JB et al. 2018. Prevotella-to-Bacteroides ratio predicts body weight and fat loss success on 24-week diets varying in macronutrient composition and dietary fiber: results from a post-hoc analysis. Int. J. Obes. 43:149–57
    [Google Scholar]
  74. Holmes ZC, Villa MM, Durand HK, Jiang S, Dallow EP. 2022. Microbiota responses to different prebiotics are conserved within individuals and associated with habitual fiber intake. Microbiome 10:114
    [Google Scholar]
  75. Holscher HD. 2017. Dietary fiber and prebiotics and the gastrointestinal microbiota. Gut Microbes 8:172–84
    [Google Scholar]
  76. Holscher HD, Bauer LL, Gourineni V, Pelkman CL, Fahey GC, Swanson KS. 2015. Agave inulin supplementation affects the fecal microbiota of healthy adults participating in a randomized, double-blind, placebo-controlled, crossover trial. J. Nutr. 145:2025–32
    [Google Scholar]
  77. Hughes RL, Kable ME, Marco M, Keim NL. 2019a. The role of the gut microbiome in predicting response to diet and the development of precision nutrition models. Part II. Results. Adv. Nutr. 10:979–98
    [Google Scholar]
  78. Hughes RL, Marco ML, Hughes JP, Keim NL, Kable ME. 2019b. The role of the gut microbiome in predicting response to diet and the development of precision nutrition models. Part I. Overview of current methods. Adv. Nutr. 10:953–78
    [Google Scholar]
  79. Iadanza E, Fabbri R, Bašić-Čičak D, Amedei A, Telalovic JH. 2020. Gut microbiota and artificial intelligence approaches: a scoping review. Health Technol. 10:1343–58
    [Google Scholar]
  80. Inst. Med 2005. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids Washington, DC: Natl. Acad.
  81. Jie Z, Yu X, Liu Y, Sun L, Chen P et al. 2021. The baseline gut microbiota directs dieting-induced weight loss trajectories. Gastroenterology 160:2029–42
    [Google Scholar]
  82. Joglekar P, Sonnenburg ED, Higginbottom SK, Earle KA, Morland C et al. 2018. Genetic variation of the SusC/SusD homologs from a polysaccharide utilization locus underlies divergent fructan specificities and functional adaptation in Bacteroides thetaiotaomicron strains. mSphere 3:00185
    [Google Scholar]
  83. Johnson AJ, Vangay P, Al-Ghalith GA, Hillmann BM, Ward TL et al. 2019. Daily sampling reveals personalized diet–microbiome associations in humans. Cell Host Microbe 25:789–802
    [Google Scholar]
  84. Johnson AJ, Zheng JJ, Kang JW, Saboe A, Knights D, Zivkovic AM. 2020. A guide to diet–microbiome study design. Front. Nutr. 7:79
    [Google Scholar]
  85. Johnson JS, Spakowicz DJ, Hong BY, Petersen LM, Demkowicz P et al. 2019. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10:5029
    [Google Scholar]
  86. Karlsson FH, Tremaroli V, Nookaew I, Bergström G, Behre CJ et al. 2013. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498:99–103
    [Google Scholar]
  87. Katagiri R, Goto A, Sawada N, Yamaji T, Iwasaki M et al. 2020. Dietary fiber intake and total and cause-specific mortality: the Japan Public Health Center–based prospective study. Am. J. Clin. Nutr. 111:1027–35
    [Google Scholar]
  88. Katoh T, Ojima MN, Sakanaka M, Ashida H, Gotoh A, Katayama T. 2020. Enzymatic adaptation of Bifidobacterium bifidum to host glycans, viewed from glycoside hydrolyases and carbohydrate-binding modules. Microorganisms 8:481
    [Google Scholar]
  89. Kavitha KR, Ram AV, Anandu S, Karthik S, Kailas S, Arjun NM. 2018. PCA-based gene selection for cancer classification. 2018 IEEE International Conference on Computational Intelligence and Computing Research Piscataway, NJ: IEEE
    [Google Scholar]
  90. Kim CC, Healey GR, Kelly WJ, Patchett ML, Jordens Z et al. 2019. Genomic insights from Monoglobus pectinilyticus: a pectin-degrading specialist bacterium in the human colon. ISME J. 13:1437–56
    [Google Scholar]
  91. Klimenko NS, Tyakht AV, Popenko AS, Vasiliev AS, Altukhov IA et al. 2018. Microbiome responses to an uncontrolled short-term diet intervention in the frame of the Citizen Science Project. Nutrients 10:576
    [Google Scholar]
  92. Kolida S, Meyer D, Gibson GR. 2007. A double-blind placebo-controlled study to establish the bifidogenic dose of inulin in healthy humans. Eur. J. Clin. Nutr. 61:1189–95
    [Google Scholar]
  93. Kong C, Faas MM, de Vos P, Akkerman R. 2020. Impact of dietary fibers in infant formulas on gut microbiota and the intestinal immune barrier. Food Funct. 11:9445–67
    [Google Scholar]
  94. Koropatkin NM, Cameron EA, Martens EC. 2012. How glycan metabolism shapes the human gut microbiota. Nat. Rev. Microbiol. 10:323–35
    [Google Scholar]
  95. Korpela K, Flint HJ, Johnstone AM, Lappi J, Poutanen K et al. 2014. Gut microbiota signatures predict host and microbiota responses to dietary interventions in obese individuals. PLOS ONE 9:e90702
    [Google Scholar]
  96. Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F et al. 2015. Dietary fiber–induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 22:971–82
    [Google Scholar]
  97. Kundi ZM, Lee JC-Y, Pihlajamäki J, Chan CB, Leung KS et al. 2021. Dietary fiber from oat and rye brans ameliorate Western diet–induced body weight gain and hepatic inflammation by the modulation of short-chain fatty acids, bile acids, and tryptophan metabolism. Mol. Nutr. Food Res. 65:1900580
    [Google Scholar]
  98. Lagier JC, Dubourg G, Million M, Cadoret F, Bilen M et al. 2018. Culturing the human microbiota and culturomics. Nat. Rev. Microbiol. 16:540–50
    [Google Scholar]
  99. Leeming ER, Louca P, Gibson R, Menni C, Spector TD, Le Roy CI 2021. The complexities of the diet-microbiome relationship: advances and perspectives. Genome Med. 13:10
    [Google Scholar]
  100. Liu S, Fang Z, Wang H, Zhai Q, Hang F et al. 2021. Gene–phenotype associations involving human-residential bifidobacteria (HRB) reveal significant species- and strain-specificity in carbohydrate catabolism. Microorganisms 9:883
    [Google Scholar]
  101. Liu X, Yang W, Petrick JL, Liao LM, Wang W et al. 2021. Higher intake of whole grains and dietary fiber are associated with lower risk of liver cancer and chronic liver disease mortality. Nat. Commun. 12:6388
    [Google Scholar]
  102. Liu Z, de Vries B, Gerritsen J, Smidt H, Zoetendal EG. 2020. Microbiome-based stratification to guide dietary interventions to improve human health. Nutr. Res. 82:1–10
    [Google Scholar]
  103. Lockwood MB, Green SJ. 2020. Clinical care is evolving: the microbiome for advanced practice nurses. J. Am. Assoc. Nurse Pract. 32:290–92
    [Google Scholar]
  104. Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B 2014. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42:D490–95
    [Google Scholar]
  105. Loomba R, Seguritan V, Li W, Long T, Klitgord N et al. 2017. Gut microbiome–based metagenomic signature for non-invasive detection of advanced fibrosis in human nonalcoholic fatty liver disease. Cell Metab. 25:1054–62
    [Google Scholar]
  106. Ma C, Wasti S, Huang S, Zhang Z, Mishra R et al. 2020. The gut microbiome stability is altered by probiotic ingestion and improved by the continuous supplementation of galactooligosaccharide. Gut Microbes 12:e1785252
    [Google Scholar]
  107. Magne F, Gotteland M, Gauthier L, Zazueta A, Pesoa S et al. 2020. The Firmicutes/Bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients?. Nutrients 12:1474
    [Google Scholar]
  108. Makki K, Deehan EC, Walter J, Bäckhed F. 2018. The impact of dietary fiber on gut microbiota in host, health and disease. Cell Host Microbe 23:705–15
    [Google Scholar]
  109. Maldonado-Gómez MX, Martínez I, Bottacini F, O'Callaghan A, Ventura M et al. 2016. Stable engraftment of Bifidobacterium longum AH1206 in the human gut depends on individualized features of the resident microbiome. Cell Host Microbe 20:515–26
    [Google Scholar]
  110. Mallick H, Franzosa EA, McIver LJ, Banerjee S, Sirota-Madi A et al. 2019. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 10:3136
    [Google Scholar]
  111. Mancabelli L, Milani C, Lugli GA, Turroni F, Ferrario C et al. 2017. Meta-analysis of the human gut microbiome from urbanized and pre-agricultural populations. Environ. Microbiol. 19:1379–90
    [Google Scholar]
  112. Martínez I, Kim J, Duffy PR, Schlegel VL, Walter J. 2010. Resistant starches types 2 and 4 have differential effects on the composition of the fecal microbiota in human subjects. PLOS ONE 5:e15046
    [Google Scholar]
  113. Martínez I, Stegen JC, Maldonado-Gómez MX, Eren AM, Siba PM et al. 2015. The gut microbiota of rural Papua New Guineans: composition, diversity patterns, and ecological processes. Cell Rep. 11:527–38
    [Google Scholar]
  114. Medawar E, Haange SB, Rolle-Kampczyk U, Engelmann B, Dietrich A et al. 2021. Gut microbiota link dietary fiber intake and short-chain fatty acid metabolism with eating behavior. Transl. Psychiatry 11:500
    [Google Scholar]
  115. Mendes-Soares H, Raveh-Sadka T, Azulay S, Edens K, Ben-Shlomo Y et al. 2019. Assessment of a personalized approach to prediction postprandial glycemic responses to food among individuals without diabetes. JAMA Netw. Open 2:e188102
    [Google Scholar]
  116. Mobeen F, Sharma V, Tulika P. 2018. Enterotype variations of the healthy human gut microbiome in different geographical regions. Bioinformation 14:560–73
    [Google Scholar]
  117. Moeller AH. 2017. The shrinking human gut microbiome. Curr. Opin. Microbiol. 38:30–35
    [Google Scholar]
  118. Munoz J, James K, Bottacini F, Van Sinderen D. 2020. Biochemical analysis of cross-feeding behaviour between two common gut commensals when cultivated on plant-derived arabinogalactan. Microb. Biotechnol. 13:1733–47
    [Google Scholar]
  119. Muñoz Pedrogo DA, Jensen MD, Van Dyke CT, Murray JA, Woods JA et al. 2018. Gut microbial carbohydrate metabolism hinders weight loss in overweight adults undergoing lifestyle intervention with a volumetric diet. Mayo Clin. Proc. 93:1104–10
    [Google Scholar]
  120. Nagata N, Nishijima S, Kojima Y, Hisada Y, Imbe K et al. 2022. Metagenomic identification of microbial signatures predicting pancreatic cancer from a multinational study. Gastroenterology 163:222–38
    [Google Scholar]
  121. Namkung J. 2020. Machine learning methods for microbiome studies. J. Microbiol. 58:206–16
    [Google Scholar]
  122. Ndeh D, Gilbert HJ. 2018. Biochemistry of complex glycan depolymerisation by the human gut microbiota. FEMS Microbiol. Rev. 42:146–64
    [Google Scholar]
  123. Nguyen NK, Deehan EC, Zhang Z, Jin M, Baskota N et al. 2020. Gut microbiota modulation with long-chain corn bran arabinoxylan in adults with overweight and obesity is linked to an individualized temporal increase in fecal propionate. Microbiome 8:118
    [Google Scholar]
  124. O'Toole PW, Marchesi JR, Hill C. 2017. Next-generation probiotics: the spectrum from probiotics to live biotherapeutics. Nat. Microbiol. 2:17057
    [Google Scholar]
  125. Octaria EA, Siswantining T, Bustamam A, Sarwinda D. 2020. Kernel PCA and SVM-RFE based feature selection for classification of dengue microarray dataset. AIP Conf. Proc. 2264:030004
    [Google Scholar]
  126. Oh H-S, Min U, Jang H, Kim N, Lim J et al. 2022. Proposal of a health gut microbiome index based on a meta-analysis of Korean and global population datasets. J. Microbiol. 60:533–49
    [Google Scholar]
  127. Oh TG, Kim SM, Caussy C, Fu T, Guo J et al. 2020. A universal gut-microbiome-derived signature predicts cirrhosis. Cell Metab. 32:878–88
    [Google Scholar]
  128. Ojima MN, Yoshida K, Sakanaka M, Jiang L, Odamaki T, Katayama T. 2022. Ecological and molecular perspectives on responders and non-responders to probiotics and prebiotics. Curr. Opin. Biotechnol. 73:108–20
    [Google Scholar]
  129. Parkar SG, Frost JKT, Rosendale D, Stoklosinski HM, Jobsis CMH et al. 2021. The sugar composition of the fibre in selected plant foods modulates weaning infants’ gut microbiome composition and fermentation metabolites in vitro. Sci. Rep. 11:9292
    [Google Scholar]
  130. Parnell JA, Reimer RA. 2012. Prebiotic fibres dose-dependently increase satiety hormones and alter Bacteroidetes and Firmicutes in lean and obese JCR:LA-cp rats. Br. J. Nutr. 107:601–13
    [Google Scholar]
  131. Pasolli E, Truong DT, Malik F, Waldron L, Segata N. 2016. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLOS Comput Biol. 12:e1004977
    [Google Scholar]
  132. Pisanu S, Palmas V, Madau V, Casula E, Deledda A et al. 2020. Impact of a moderately hypocaloric Mediterranean diet on the gut microbiota composition of Italian obese patients. Nutrients 12:2707
    [Google Scholar]
  133. Poeker SA, Geirnaert A, Berchtold L, Greppi A, Krych L et al. 2018. Understanding the prebiotic potential of different dietary fibers using an in vitro continuous adult fermentation model (PolyFermS). Sci. Rep. 8:4318
    [Google Scholar]
  134. Probert HM, Apajalahti JHA, Rautonen N, Stowell J, Gibson GR. 2004. Polydextrose, lactitol, and fructo-oligosaccharide fermentation by colonic bacteria in a three-stage continuous culture system. Appl. Environ. Microbiol. 70:4505–11
    [Google Scholar]
  135. Qu K, Gao F, Guo F, Zou Q. 2019. Taxonomy dimension reduction for colorectal cancer prediction. Comput. Biol. Chem. 83:107160
    [Google Scholar]
  136. Quince C, Delmont TO, Raguideau S, Alneberg J, Darling AE et al. 2017. DESMAN: a new tool for de novo extraction of strains from metagenomes. Genome Biol. 18:181
    [Google Scholar]
  137. Quince C, Nurk S, Raguideau S, James R, Soyer OS et al. 2021. STRONG: metagenomics strain resolution on assembly graphs. Genome Biol. 22:214
    [Google Scholar]
  138. Rakoff-Nahoum S, Foster KR, Comstock LE. 2016. The evolution of cooperation within the gut microbiota. Nature 533:255–59
    [Google Scholar]
  139. Ramos-Romero S, Léniz A, Martínez-Maqueda D, Amézqueta S, Fernández-Quintela A et al. 2021. Inter-individual variability in insulin response after grape pomace supplementation in subjects at high cardiometabolic risk: role of microbiota and miRNA. Mol. Nutr. Food Res. 65:e2000113
    [Google Scholar]
  140. Rampelli S, Schnorr SL, Consolandi C, Turroni S, Severgnini M et al. 2015. Metagenome sequencing of the Hadza hunter-gatherer gut microbiota. Curr. Biol. 25:1682–93
    [Google Scholar]
  141. Reichardt N, Vollmer M, Holtrop G, Farquharson FM, Wefers D et al. 2018. Specific substrate-driven changes in human faecal microbiota composition contrast with functional redundancy in short-chain fatty acid production. ISME J. 12:610–22
    [Google Scholar]
  142. Rivière A, Moens F, Selak M, Maes D, Weckx S, De Vuyst L. 2014. The ability of bifidobacteria to degrade arabinoxylan oligosaccharide constituents and derived oligosaccharides is strain dependent. Appl. Environ. Microbiol. 80:204–17
    [Google Scholar]
  143. Rivière A, Selak M, Lantin D, Leroy F, De Vuyst L. 2016. Bifidobacteria and butyrate-producing colon bacteria: importance and strategies for their stimulation in the human gut. Front. Microbiol. 7:979
    [Google Scholar]
  144. Rodriguez J, Hiel S, Neyrinck AM, Le Roy T, Pötgens SA et al. 2020. Discovery of the gut microbial signature driving the efficacy of prebiotic intervention in obese patients. Gut 69:1975–87
    [Google Scholar]
  145. Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T et al. 2018. Environment dominates over host genetics in shaping human gut microbiota. Nature 555:210–15
    [Google Scholar]
  146. Saito Y, Shigehisa A, Watanabe Y, Tsukuda N, Moriyama-Ohara K et al. 2020. Multiple transporters and glycoside hydrolases are involved in arabinoxylan derived oligosaccharide utilization in Bifidobacterium pseudocatenulatum. Appl. Environ. Microbiol. 86:e01782
    [Google Scholar]
  147. Salonen A, Lahti L, Salojärvi J, Holtrop G, Korpela K et al. 2014. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. ISME J. 8:2218–30
    [Google Scholar]
  148. Sandberg J, Kovatcheva-Datchary P, Björck I, Bäckhed F, Nilsson A. 2019. Abundance of gut Prevotella at baseline and metabolic response to barley prebiotics. Eur. J. Nutr. 58:2365–76
    [Google Scholar]
  149. Sanders ME, Benson A, Lebeer S, Merenstein DJ, Klaenhammer TR. 2018. Shared mechanisms among probiotic taxa: implications for general probiotic claims. Curr. Opin. Biotechnol. 49:207–16
    [Google Scholar]
  150. Schnorr SL, Candela M, Rampelli S, Centanni M, Consolandi C et al. 2014. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5:3654
    [Google Scholar]
  151. Schroeder BO, Birchenough GMH, Ståhlman M, Arike L, Johansson MEV et al. 2018. Bifidobacteria or fiber protects against diet-induced microbiota-mediated colonic mucus deterioration. Cell Host Microbe 23:27–40.e7
    [Google Scholar]
  152. Schupack DA, Mars RAT, Voelker DH, Abeykoon JP, Kashyap PC. 2022. The promise of the gut microbiome as part of individualized treatment strategies. Nat. Rev. Gastroenterol. Hepatol 19:7–25
    [Google Scholar]
  153. Segata N. 2015. Gut microbiome: westernization and the disappearance of intestinal diversity. Curr. Biol. 25:R611–13
    [Google Scholar]
  154. Sheridan PO, Martin JC, Lawley TD, Browne HP, Harris HMB et al. 2015. Polysaccharide utilization loci and nutritional specialization in a dominant group of butyrate-producing human colonic Firmicutes. Microb. Genom. 2:e000043
    [Google Scholar]
  155. Singh RK, Chang H-W, Yan D, Lee KM, Ucmak D et al. 2017. Influence of diet on the gut microbiome and implications for human health. J. Transl. Med. 15:73
    [Google Scholar]
  156. So D, Yao CK, Gill PA, Pillai N, Gibson PR, Muir JG. 2021. Screening dietary fibres for fermentation characteristics and metabolic profiles using a rapid in vitro approach: implications for irritable bowel syndrome. Br. J. Nutr. 126:208–18
    [Google Scholar]
  157. Song S, Song YJ. 2021. Dietary fiber and its source are associated with cardiovascular risk factors in Korean adults. Nutrients 13:160
    [Google Scholar]
  158. Sonnenburg ED, Smits SA, Tikhonov M, Higginbottom SK, Wingreen NS et al. 2016. Diet-induced extinctions in the gut microbiota compound over generations. Nature 529:212–15
    [Google Scholar]
  159. Sonnenburg ED, Sonnenburg JL. 2014. Starving our microbial self: the deleterious consequences of a diet deficient in microbiota-accessible carbohydrates. Cell Metab. 20:779–86
    [Google Scholar]
  160. Sonnenburg ED, Zheng H, Joglekar P, Higginbottom SK, Firbank SJ et al. 2010. Specificity of polysaccharide use in intestinal Bacteroides species determines diet-induced microbiota alterations. Cell 141:1241–52
    [Google Scholar]
  161. Sonnenburg JL, Xu J, Leip DD, Chen C-H, Westover BP et al. 2005. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science 307:1955–59
    [Google Scholar]
  162. Soverini M, Rampelli S, Turroni S, Schnorr SL, Quercia S et al. 2016. Variations in the post-weaning human gut metagenome profile as result of Bifidobacterium acquisition in the Western microbiome. Front. Microbiol. 7:1058
    [Google Scholar]
  163. Soverini M, Turroni S, Biagi E, Quercia S, Brigidi P et al. 2017. Variation of carbohydrate-active enzyme patterns in the gut microbiota of Italian healthy subjects and type 2 diabetes patients. Front. Microbiol. 8:2079
    [Google Scholar]
  164. Sun Y, Fan X, Zhao J 2022. Development of colorectal cancer detection and prediction based on gut microbiome big data. Med. Microecol. 12:100053
    [Google Scholar]
  165. Swanson KS, de Vos WM, Martens EC, Gilbert JA, Menon RS et al. 2020. Effect of fructans, prebiotics and fibres on the human gut microbiome assessed by 16S rRNA–based approaches: a review. Benef. Microbes 11:101–29
    [Google Scholar]
  166. Tamburini F, Maghini D, Oduaran OH, Brewster R, Hulley MR et al. 2022. Short- and long-read metagenomics of urban and rural South African gut microbiomes reveal a transitional composition and undescribed taxa. Nat. Comm. 13:926
    [Google Scholar]
  167. Tanes C, Bittinger K, Gao Y, Friedman ES, Nessel L et al. 2021. Role of dietary fiber in the recovery of the human gut microbiome and its metabolome. Cell Host Microbe 29:394–407
    [Google Scholar]
  168. Thomas AM, Manghi P, Asnicar F, Pasolli E, Armanini F et al. 2019. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25:667–78
    [Google Scholar]
  169. Topçuoğlu BD, Lesniak NA, Ruffin MT, Wiens J, Schloss PD. 2020. A framework for effective application of machine learning to microbiome-based classification problems. mBio 11:e00434
    [Google Scholar]
  170. Tuohy KM, Kolida S, Lustenberger AM, Gibson GR. 2001. The prebiotic effects of biscuits containing partially hydrolysed guar gum and fructo-oligosaccharides—a human volunteer study. Br. J. Nutr. 86:341–48
    [Google Scholar]
  171. Turroni F, Strati F, Foroni E, Serafini F, Duranti S et al. 2012. Analysis of predicted carbohydrate transport systems encoded by Bifidobacterium bifidum PRL2010. Appl. Environ. Microbiol. 78:5002–12
    [Google Scholar]
  172. USDA Agric. Res. Serv 2021. Usual nutrient intake from food and beverages, by gender and age. What we eat in America: NHANES 2015–2018 Data Tables USDA Agric. Res. Serv. Beltsville, MD:
  173. Valdez-Palomares F, Nambo-Venegas R, Uribe-García J, Mendoza-Vargas A, Granados-Portillo O et al. 2021. Intestinal microbiota fingerprint in subjects with irritable bowel syndrome responders to a low FODMAP diet. Food Funct. 12:3206–18
    [Google Scholar]
  174. Van Den Abbeele P, Duysburgh C, Ghyselinck J, Goltz S, Berezhnaya Y et al. 2021. Fructans with varying degree of polymerization enhance the selective growth of Bifidobacterium animalis subsp. lactis BB-12 in the human gut microbiome in vitro. Appl. Sci. 11:598
    [Google Scholar]
  175. Van Rossum T, Ferretti P, Maistrenko OM, Bork P. 2020. Diversity within species: interpreting strains in microbiomes. Nat. Rev. Microbiol. 18:491–506
    [Google Scholar]
  176. Vangay P, Hillmann BM, Knights D. 2019. Microbiome Learning Repo (ML Repo): a public repository of microbiome regression and classification tasks. Gigascience 8:giz042
    [Google Scholar]
  177. Vangay P, Johnson AJ, Ward TL, Al-Ghalith GA, Shields-Cutler RR et al. 2018. US immigration westernizes the human gut microbiome. Cell 175:962–72
    [Google Scholar]
  178. Vatanen T, Plichta DR, Somani J, Münch PC, Arthur TD et al. 2019. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nat. Microbiol. 4:470–79
    [Google Scholar]
  179. Villa MM, Bloom RJ, Silverman JD, Durand HK, Jiang S et al. 2020. Interindividual variation in dietary carbohydrate metabolism by gut bacteria revealed with droplet microfluidic culture. mSystems 5:e00864
    [Google Scholar]
  180. Wang Q, Wang K, Wu W, Giannoulatou E, Ho JWK, Li L. 2019. Host and microbiome multi-omics integration: applications and methodologies. Biophys. Rev. 11:55–65
    [Google Scholar]
  181. Wang S, Xiao Y, Tian F, Zhao J, Zhang H et al. 2020. Rational use of prebiotics for gut microbiota alterations: specific bacterial phylotypes and related mechanisms. J. Funct. Foods 66:103838
    [Google Scholar]
  182. Watanabe Y, Saito Y, Hara T, Tsukuda N, Aiyama-Suzuki Y et al. 2021. Xylan utilisation promotes adaptation of Bifidobacterium pseudocatenulatum to the human gastrointestinal tract. ISME Commun. 1:62
    [Google Scholar]
  183. Wenzel TJ, Gates EJ, Ranger AL, Klegeris A. 2020. Short-chain fatty acids (SCFAs) alone or in combination regulate select immune functions of microglia-like cells. Mol. Cell. Neurosci. 105:103493
    [Google Scholar]
  184. Wexler AG, Goodman AL. 2017. An insider's perspective: Bacteroides as a window into the microbiome. Nat. Microbiol. 2:17026
    [Google Scholar]
  185. Ye S, Shah BR, Li J, Liang H, Zhan F et al. 2022. A critical review on interplay between dietary fibers and gut microbiota. Trends Food Sci. Technol. 124:237–49
    [Google Scholar]
  186. Yoshida K, Hirano R, Sakai Y, Choi M, Sakanaka M et al. 2021. Bifidobacterium response to lactulose ingestion in the gut relies on a solute-binding protein–dependent ABC transporter. Commun. Biol. 4:541
    [Google Scholar]
  187. Yu D, Zhao L, Zhao W. 2020. Status and trends in consumption of grains and dietary fiber among Chinese adults (1982–2015). Nutr. Rev. 78:Suppl. 143–53
    [Google Scholar]
  188. Ze X, Duncan SH, Louis P, Flint HJ 2012. Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. ISME J. 6:1535–43
    [Google Scholar]
  189. Zeevi D, Korem T, Zmora N, Halpern Z, Elinav E et al. 2015. Personalized nutrition by prediction of glycemic responses. Cell 163:1079–94
    [Google Scholar]
  190. Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M et al. 2016. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352:565–69
    [Google Scholar]
  191. Zhou Y-H, Gallins P. 2019. A review and tutorial of machine learning methods for microbiome host trait prediction. Front. Genet. 10:579
    [Google Scholar]
/content/journals/10.1146/annurev-food-060721-015516
Loading
/content/journals/10.1146/annurev-food-060721-015516
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error