Glycemic Effect of Oat and Barley Beta-glucan When Incorporated into a Snack Bar: A Dose Escalation Study
Bibliographic record
Abstract
BACKGROUND: The blood glucose-lowering effects of β-glucan from oats and barley depend on the amounts consumed and their rheological properties. This has been recently challenged with growing evidence that the food matrix may also be an important factor in predicting its physiological response. OBJECTIVE: The objective of this study was to examine the effects of varying doses of β-glucan from oats and barley and added to a snack bar on postprandial glycemia. DESIGN: In a randomized crossover study, 12 healthy males and females consumed one of 8 snack bars containing 0 (control), 1.5, 3, and 6 g of β-glucan derived from oats or barley or 3 white bread controls. All treatments contained 50 g of available carbohydrate. Blood glucose concentrations were measured after ingestion of the treatments over 2 hours. RESULTS: Incorporation of 1.5 to 6 g of β-glucan into snack bars had no additional glucose-lowering benefits irrespective of dose and source compared to the control bars (0 g β-glucan), suggesting that both the solid food matrix and composition of the bars may play a role in their effects on glycemic response. All bars reduced blood glucose area under the curve (AUC) by an average of 25% (p < 0.05) compared to the mean of the 3 white bread controls. CONCLUSION: Adding β-glucan from oats and barley to the snack bar formulation used in this study did not yield any additional benefits beyond the glucose-lowering effects of the snack bars themselves.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".