Effect of polysaccharide-induced viscosity on the digestion of proteins, fats, and carbohydrates in food: A comprehensive review and future perspectives
Bibliographic record
Abstract
Polysaccharides play a crucial role in slowing macronutrient digestion, contributing to satiety, glycemic control, regulating blood sugar levels, and cholesterol management. Their impact on food digestion and absorption is largely mediated by their ability to increase the viscosity of chyme and digesta, affecting the activity of digestive enzymes. This review examines the effects of polysaccharides on digestive enzymes, focusing on their inhibition of proteolysis, lipolysis, and amylolysis. While other ingredients like gelatin or specific food additives can increase food viscosity, this review specifically emphasizes polysaccharides, particularly soluble fibers. A comprehensive search in Web of Science/ScienceDirect identified 1589 articles published between January 1982 and August 2023. After applying selection criteria, 212 trials from 96 articles that directly examined the influence of polysaccharide viscosity on macronutrient digestibility were included. The review identifies 38 polysaccharides, including pectin, xanthan, guar gum, carboxymethylcellulose, carrageenan, and konjac glucomannan, known for their viscosity-enhancing properties. These polysaccharides impact nutrient digestion through several mechanisms: they reduce diffusion and mass transfer, impede mixing of digestive components, block enzyme active sites, induce conformational changes, and form aggregates and surface bonds that immobilize substrates. The extent of digestion inhibition is influenced by factors such as polysaccharide concentration, viscosity, and molecular structure, as well as the properties of the substrate, including molecular weight and conformation. The review highlights the need for more accurate modeling of digestive processes and in vitro systems that effectively replicate digestive conditions to better understand the impact of polysaccharides on nutrient digestion and absorption. Future research should explore complex systems, including whole foods and fiber-rich by-products like fruit peels or grain husks containing both soluble and insoluble fiber, to gain deeper insights into how polysaccharides affect into nutrient digestion and absorption in real-world scenarios. • Review explores how polysaccharides affect proteolysis, lipolysis, and amylolysis. • Polysaccharides enhance viscosity, impacting macronutrient digestion and absorption. • Viscosity depends on polysaccharide concentration, structure, and substrate properties. • Polysaccharides slow digestion by reducing enzyme diffusion and blocking their action. • Exploration of polysaccharide-rich food waste can boost sustainability and nutrition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".