Effect of 6 weeks' consumption of β-glucan-rich oat products on cholesterol levels in mildly hypercholesterolaemic overweight adults
Bibliographic record
Abstract
Several regulatory bodies have approved a health claim on the cholesterol-lowering effects of oat β-glucan at levels of 3·0 g/d. The present study aimed to test whether 1·5 g/d β-glucan provided as ready-to-eat oat flakes was as effective in lowering cholesterol as 3·0 g/d from oats porridge. A 6-week randomised controlled trial was conducted in eighty-seven mildly hypercholesterolaemic ( ≥ 5 mmol/l and < 7·5 mmol/l) men and women assigned to one of three diet arms (25 % energy (E%) protein; 45 E% carbohydrate; 30 E% fat, at energy requirements for weight maintenance): (1) minimal β-glucan (control); (2) low-dose oat β-glucan (1·5 g β-glucan; oats low - OL) or (3) higher dose oat β-glucan (3·0 g β-glucan; oats high - OH). Changes in total cholesterol and LDL-cholesterol (LDL-C) from baseline were assessed using a linear mixed model and repeated-measures ANOVA, adjusted for weight change. Total cholesterol reduced significantly in all groups ( - 7·8 (sd 13·8) %, - 7·2 (sd 12·4) % and - 5·5 (sd 9·3) % in the OH, OL and control groups), as did LDL-C ( - 8·4 (sd 18·5) %, - 8·5 (sd 18·5) % and - 5·5 (sd 12·4) % in the OH, OL and control groups), but between-group differences were not significant. In responders only (n 60), β-glucan groups had higher reductions in LDL-C ( - 18·3 (sd 11·1) % and - 18·1 (sd 9·2) % in the OH and OL groups) compared with controls ( - 11·7 (sd 7·9) %; P = 0·044). Intakes of oat β-glucan were as effective at doses of 1·5 g/d compared with 3 g/d when provided in different food formats that delivered similar amounts of soluble β-glucan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".