The effect of barley beta-glucan concentrate on LDL-cholesterol and other risk factors for cardiovascular disease
Bibliographic record
Abstract
Conclusions. These studies suggest that although the beta-glucan concentrate used in these studies may have beneficial hypoglycemic effects when administered in a beverage, long-term intake could not lower LDL-c relative to placebo. Background. beta-glucan, a soluble dietary fibre found in barley, can reduce postprandial glycemia and serum lipids, thereby lowering risk of type 2 diabetes and cardiovascular disease. It is not clear however what amount of beta-glucan is required to significantly improve glycemic response and lipid profile, and further how the mode of administration affects the physiological response. The current thesis examines the dose-dependant effect of a barley beta-glucan concentrate on postprandial glycemia and lipid profile, and whether this affect is dependant on the mode of administration. Results. Study 1 demonstrated a dose-dependant effect of a beta-glucan beverage in attenuating postprandial glycemic response. In study 2, multiple doses of beta-glucan given in bars had no effect compared to control. In study 3, two doses of beta-glucan resulted in no effect on lipid profile. Design. To address these objectives, two double-blinded, acute, crossover randomized clinical trials (RCTs) (study 1 2) were conducted in healthy individuals and one long-term doubleblinded, two-centre, parallel RCT (study 3) was conducted in hypercholesterolemic individuals. In study 1 and 2, different doses of beta-glucan were examined using different modes of administration. In study 3, the effect of 6-week intake of different doses beta-glucan was examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".