Lower 30 Minute Serum Insulin in Healthy Sprague‐Dawley Rats Consuming Chips from Specific Barley Flour Blends
Bibliographic record
Abstract
ABSTRACT Consumption of whole grains such as barley is known to inversely correlate with insulin resistance, type 2 diabetes, and other chronic diseases. However, data from randomized controlled trials in humans have not produced consistent results. Compositional differences between foods produced from different whole grains are likely to be a main reason for these discrepancies. The purpose of this study was to determine if varying barley flour composition achieved through milling influenced the postprandial glucose and insulin response. Specifically, this study aimed to test barley foods in the form of chips with very low hydration, thereby mimicking human snack foods. Fasted rats (N = 39) were randomized to one of four barley treatments, all produced from a single variety, Hordeum vulgare L. ‘CDC Fibar.’ The treatments used were straight‐grade flour (SGF), whole grain flour (WGF), bran flour with high β‐glucan (BF‐BG), bran flour with high insoluble dietary fiber, and wheat flour (WF) as the control. Blood was sampled over 120 min following treatment consumption to measure postprandial glucose and insulin concentrations. Our data showed that although there were no substantial glycemic or insulin effects following one‐time consumption of barley tortilla chips with specific compositions, those rats consuming WGF, SGF, or BF‐BG tended to have lower 30 min serum insulin concentrations compared with those rats consuming WF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".