β-Glucan from Two Sources of Oat Concentrates Affect Postprandial Glycemia in Relation to the Level of Viscosity
Bibliographic record
Abstract
OBJECTIVE: Soluble dietary fiber has been shown to attenuate the postprandial rise in blood glucose levels and reduce the risk of type 2 diabetes and cardiovascular disease. This effect seems to be related to its rheological properties including viscosity. We examined the intra-fiber variability between two different processing methods of concentrating beta-glucan from oats (aqueous vs. enzymatic) in relation to the level of viscosity of beta-glucan and its effect on postprandial glycemia in healthy individuals. DESIGN: In an acute, randomized, double-blind, crossover study, 11 healthy subjects (gender: 5M:6F; age: 34 +/- 5 years; BMI: 23 +/- 0.8 kg/m(2)) were randomly assigned, on three separate occasions, to consume one of three fiber-matched treatments along with a 75 g oral glucose drink. The enzymatically processed beta-glucan (Oat-A) differed from beta-glucan processed through the aqueous method (Oat-B) solely with regard to viscosity. Finger-prick capillary blood samples were obtained at fasting and at 15, 30, 45, 60, 90 and 120 min after the start of the test drink. The viscosities of the fiber drinks were determined (Paar Physica UDS200 viscometer). RESULTS: Rheological measurements demonstrated that Oat-A had a significantly higher viscosity than Oat-B and control at 5, 15, 30, 60, and 120 min (p < 0.001). The incremental area under the glucose curve (AUC) on Oat-A was 19.6% and 17% lower than that of Oat-B and control, respectively (p < 0.01). CONCLUSIONS: This study shows that processing oat beta-glucan through enzymatic, rather than by aqueous methods, preserves the viscosity and improves postprandial glycemic control.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".