Dietary compounds slow starch enzymatic digestion: A review
Bibliographic record
Abstract
Dietary compounds significantly affected starch enzymatic digestion. However, effects of dietary compounds on starch digestion and their underlying mechanisms have been not systematically discussed yet. This review summarized the effects of dietary compounds including cell walls, proteins, lipids, non-starchy polysaccharides, and polyphenols on starch enzymatic digestion. Cell walls, proteins, and non-starchy polysaccharides restricted starch disruption during hydrothermal treatment and the retained ordered structures limited enzymatic binding. Moreover, they encapsulated starch granules and formed physical barriers for enzyme accessibility. Proteins, non-starchy polysaccharides along with lipids and polyphenols interacted with starch and formed ordered assemblies. Furthermore, non-starchy polysaccharides and polyphenols showed robust abilities to reduce activities of α-amylase and α-glucosidase. Accordingly, it can be concluded that dietary compounds lowered starch digestion mainly by three modes: (i) prevented ordered structures from disruption and formed ordered assemblies chaperoned with these dietary compounds; (ii) formed physical barriers and prevented enzymes from accessing/binding to starch; (iii) reduced enzymes activities. Dietary compounds showed great potentials in lowering starch enzymatic digestion, thereby modulating postprandial glucose response to food and preventing or treating type II diabetes disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".