Resistant Starch reduces postprandial glycemic and insulinemic response and increases satiety in humans
Bibliographic record
Abstract
Objective To assess the dose‐response effect of adding resistant starch to drinks on postprandial glycemia, insulinemia, satiety and post‐load ad libitum food intake. Methods 22 healthy "unrestrained eaters" (13 male, 9 female, age 26±4y; BMI 23.7±2.4kg/m ² ) consumed meals containing 0g, 5g, 15g and 25g of PROMITOR(tm) RS in a randomized, double blind, crossover study. Using a visual analogue scale (VAS), subjects rated their satiety level and symptoms at 0, 15, 30, 45, 60, 90 and 120 min after eating. Blood samples were taken simultaneously. Using the satiety ratings, the satiety quotient and appetite score were calculated. Two hours after eating, subjects were given an ad libitum meal and total energy intake was recorded. Results All meals were well tolerated and no significant adverse symptoms were reported. Compared to the control, the 25g dose of RS resulted in significantly lower blood glucose (P≤0.002) and serum insulin levels at 90min and 120 min. In addition the satiety quotient for mean appetite score for the 25g RS dose was significantly higher (P≤0.05) compared to the control at 15min and 45 min. However second meal ad libitum meal total energy intake was not different between treatments. Conclusions The inclusion of RS may effectively reduce the postprandial glycemic and insulinemic response of meals and increase satiety without adverse symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".