Reduction of the glycemic index by a Novel Viscous Polysaccharide when added or incorporated into commonly consumed foods.
Bibliographic record
Abstract
Background/Objectives Reductions of postprandial glucose levels have been demonstrated previously with the addition of a novel viscous polysaccharide (NVP) to a glucose drink and standard white bread. This study explores whether these reductions are sustained when NVP is added to a range of commonly consumed foods. Subjects/Methods Ten healthy subjects (4M, 6F; age 37.3 ± 3.6 y; BMI 23.8 ± 1.3 kg/m²), participated in an acute randomized controlled study. The glycemic response to cornflakes+milk (Cornfl), rice (Rice), yogurt (Yog), a frozen turkey dinner (Turk) with and without 5g of NVP sprinkled on the test meal was determined. In addition, 3 granolas with different levels of NVP , three control white breads, and one white bread and milk were also consumed. Capillary blood samples were taken fasting and at 15, 30, 45, 60, 90 and 120 min after the start of the meal. To quantify the reduction in glycemia the glycemic index (GI) was calculated. Results Addition of NVP reduced blood glucose response irrespective of food (p<0.01). The GI of Cornfl, Cornfl+NVP, Rice, Rice+NVP, Yog, Yog+NVP, Turk, Turk+NVP was 83, 58, 82, 45, 55 ,41, 44, and 38 respectively. The GI of the control granola, and granola with 2.5 and 5g of NVP were 64, 33, and 22 respectively. Conclusion Sprinkling or incorporation of NVP into a variety of different foods is highly effective in lowering the GI of a food.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".