β‐glucan from oat and barley concentrates affect postprandial glycemia and insulinemia in relation to the level of viscosity
Bibliographic record
Abstract
Beta‐glucan from oats and barley may lower blood glucose and insulin levels and reduce the risk of type 2 diabetes. There is, however, a lack of evidence correlating the dose of β‐glucan to glycemic and insulinemic response. In a double‐blind, randomized, crossover design, 12 healthy participants(gender:3M:9F;age:31±3yrs;BMI:25±0.8kg/m2)received 8 treatments: doses of 0,2,4 and 8g of β‐glucan given in a 75g glucose drink. Intravenous blood samples were collected at 0,15,30,45,60,90 and 120min and analyzed for plasma glucose (PG) and plasma insulin (PI). Oat 8g β‐glucan significantly reduced PG at 15min(p<0.01) and at the peak compared to the 0and2g; similarly, PG was significantly reduced by the barley 8g β‐glucan at 15,30 and 90min(p<0.01) compared to 0and2g. Barley 4g β‐glucan significantly lowered PG compared to barley 0and2g at 15min. The iAUC of oat 8g β‐glucan was significantly lower than 0and2g. Similarly, the iAUC of barley 8g β‐glucan was significantly lower than 0and2g. Oat 8g β‐glucan significantly lowered PI at 15,30,45,60,90 and 120min compared to control (p<0.01). Barley 8g β‐glucan decreased PI at 15,30,45 and 90min(p<0.01). The iAUC of PI was significantly lower after the oat 8g and barley 8g β‐glucan compared to the 0,2 and 4g doses(p<0.01). This study demonstrates the glucose and insulin lowering potential of β‐glucan from both oats and barley in a dose‐dependent manner.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".