A double-blind randomised controlled trial testing the effect of a barley product containing varying amounts and types of fibre on the postprandial glucose response of healthy volunteers
Bibliographic record
Abstract
The aim of the present study was to determine if the consumption of barley tortillas varying in fibre and/or starch composition affected postprandial glucose, insulin, glucagon-like peptide-1 (GLP-1) or peptide YY concentrations. A double-blind, randomised, controlled trial was performed with twelve healthy adults. They each consumed one of five barley tortillas or a glucose drink on six individual visits separated by at least 1 week. Tortillas were made from 100% barley flour blends using five different milling fractions to achieve the desired compositions. All treatments provided 50 g of available carbohydrate and were designed to make the following comparisons: (1) low-starch amylose (0%) v. high-starch amylose (42%) with similar β-glucan and insoluble fibre content; (2) low β-glucan (4.5 g) v. medium β-glucan (7.8 g) v. high β-glucan (11.6 g) with similar starch amylose and insoluble fibre content; and (3) low insoluble fibre (7.4 g) v. high insoluble fibre (19.6 g) with similar starch amylose and β-glucan content. Blood was collected at fasting and at multiple intervals until 180 min after the first bite/sip of the test product. Amylose and insoluble fibre content did not alter postprandial glucose and insulin, but high-β-glucan tortillas elicited a lower glucose and insulin response as compared to the low-β-glucan tortillas. The tortillas with high insoluble fibre had a higher AUC for GLP-1 as compared to the tortillas with low insoluble fibre, whereas amylose and β-glucan content had no effect. Results show that processing methods can be used to optimise barley foods to reduce postprandial blood glucose responses and factors that may influence satiety.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".