Increasing the viscosity of oat β-glucan beverages by reducing solution volume does not reduce glycaemic responses
Bibliographic record
Abstract
The soluble fibre (1 → 3)(1 → 4)-β-D-glucan attenuates postprandial glycaemic responses when administered in solution. This attenuating effect is strengthened when solution viscosity is increased by increasing the β-glucan dose or molecular weight (MW). The effect of varying solution viscosity by changing solution volume, without changing the β-glucan dose or MW, on glycaemic responses was determined. A total of fifteen healthy subjects received six 50 g oral glucose beverages prepared with or without 4 g of high-MW (HMW, 580,000 g/mol) or low-MW (LMW, 145,000 g/mol) β-glucan, with a beverage volume of 250 or 600 ml. Postprandial plasma glucose concentration was measured over 2 h, and the peak blood glucose rise (PBGR) and the incremental area under the glycaemic response curve (AUC) were calculated. Subjects served as their own controls. The physico-chemical properties of the beverages were measured to examine their relationship with glycaemic response results. The HMW β-glucan beverage was more viscous and achieved greater reductions in PBGR than the glucose beverage with LMW β-glucan (P < 0·05). At the same MW, the 250 and 600 ml β-glucan beverages differed in viscosity (>9-fold difference) but not in PBGR (P > 0·05). No differences in AUC were detected among the beverages (P = 0·147). The effects of β-glucan on glycaemic response were altered by changes in beverage viscosity achieved through changes in MW but not in volume. Therefore, β-glucan dose and MW are the most vital characteristics for optimising the bioactivity of β-glucan solutions with respect to glycaemic response.
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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) | 0.002 | 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".