INDUCED FIBER VISCOSITY TRIPLES ITS EFFECT ON POSTPRANDIAL BLOOD GLUCOSE RESPONSE
Bibliographic record
Abstract
This study examined the effects of modifying fiber viscosity, by dose and treatment methods, on glycemic response. On 16 occasions, in a single‐blinded, randomized, crossover design, 8 healthy subjects (4M:4F, 33.6±3.5 yrs) consumed glucose drinks (with 25 or 50g carbohydrates (CHO)) plus psyllium (0,3,6or 9g) that was either treated (induced viscosity) or untreated (inherent viscosity). The induced viscosity treatment was a combination of heat, mechanical energy, aeration and gradual cooling. Hardness, which is highly correlated with viscosity (r=0.98, p=0.0034), was used to measure meal viscosity (Instron, Canton, MA). Log hardness was directly proportional to dose (r=0.99, p=0.0056) and heat treatment (r=0.99, p<0.0002) and inversely proportional (p=0.002) to incremental blood glucose area under the curve (AUC) at 25g (r=−1.00, p=0.0001) and 50g (r=−0.094, p=0.0004) CHO. A strong inverse correlation existed between fiber‐to‐nutrient ratio and % AUC reduction (inherent: r=−0.95, p=0.0043; induced: r=−0.098, p=0.0009). Viscosity of meals is an important predictor of postprandial glycemic response when fiber‐to‐nutrient ratio is considered. A novel procedure that treats soluble fiber to triple its viscosity and reduce postprandial blood glucose can be highly regarded by industry, since it may be easier to incorporate into foods, and also by individuals, who could consume fiber at lower quantities and still achieve health benefits. Travel grant: Inovobiologic Inc. Calgary
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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) | 0.000 | 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".