Align resistant starch structures from plant-based foods with human gut microbiome for personalized health promotion

• Published in: Critical reviews in food science and nutrition Vol. 63; no. 15; pp. 2509 - 2520
• Main Authors: Li, Cheng, Hu, Yiming
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 11.06.2023; Taylor & Francis Ltd
• Subjects: Algorithms; artificial intelligence; Bacteria; Customization; Digestive system; Feces; food science; Gastrointestinal Microbiome; Gastrointestinal tract; Gut microbiota; Health promotion; human health; Humans; in vitro fecal fermentation; Intestinal microflora; intestinal microorganisms; Intestine; Machine learning; Microbiomes; Microbiota; Microorganisms; nutrition; Plant-based foods; Resistant Starch; short-chain fatty acids; Small intestine; Starch; Starch - chemistry; Resistant starch; in vitro fecal fermentation; short-chain fatty acids; gut microbiota
• ISSN: 1040-8398; 1549-7852; 1549-7852
• DOI: 10.1080/10408398.2021.1976722

Resistant starch (RS) is beneficial for human health through its interactions with gut microbiota. However, the alignment between RS structures with gut microbiota profile and consequentially health benefits remain elusive. This review summarizes current understanding of RS complex structures and their interactions with the gut microbiota, aiming to highlight the possibility of manipulating RS structures for a targeted and predictable gut microbiota shift for human health in a personalized way. Current definition of RS types is strongly associated with starch digestion behaviors in small intestine, which does not precisely reflect their interactions with human gut microbiota. Distinct alterations of gut microbiota could be associated with the same RS type. The principles to describe the specificity of different RS structural characteristics in terms of aligning with human gut microbiota shift was proposed in this review, which could result in new definitions of RS types from the microbial perspectives. To consider the highly variable personal features, a machine-learning algorithm to integrate different personalized factors and better understand the complex interaction between RS and gut microbiota and its effects on individual health was explained. This review contains important information to bring interactions between RS and gut microbiota to translational practice.