Attenuation of glycemic responses by oat β-glucan solutions and viscoelastic gels is dependent on molecular weight distribution
Bibliographic record
Abstract
Oat β-glucan attenuates postprandial glycemic responses when solubilized to form viscous solutions. High molecular weight (MW) β-glucan is associated with high solution viscosity, which is in turn associated with lower glycemic responses. However, low MW β-glucan is also able to form viscoelastic gels. The effect of low (145,000 g mol(-1)) and high (580,000 g mol(-1)) MW β-glucan presented as liquid drinks and gels on glycemic responses was determined. Healthy subjects (n = 15) consumed 50 g glucose drinks with no β-glucan; 4 g low MW; or 4 g high MW β-glucan; and gels containing 4 g low MW; 2 g low plus 2 g high MW; or 3 g high plus 1 g low MW β-glucan. Overall, β-glucan solutions elicited lower glycemic responses than gels. For gels, peak blood glucose rise (PBGR) decreased with increasing dose of high MW β-glucan (r(2) = 0.976, P > 0.05), and PBGR for the gel with 3 g high-MW was lower than for the control (P < 0.05). However, β-glucan gels retained glucose better than solutions under in vitro analysis. Observed effects were found to be related to the rheological properties of the foods. β-Glucan solutions and not gels effectively attenuated in vivo glycemic responses.
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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".