Date of this Version
Annu. Rev. Food Sci. Technol. 2023. 14:157–82. https://doi.org/10.1146/annurev-food-060721- 015516
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.