Properties of oat β‐glucan influence its LDL cholesterol lowering effect in humans
Bibliographic record
Abstract
Consuming 3g oat β‐glucan (OBG) daily is considered sufficient to lower serum LDL cholesterol (LDL). Not all studies show this, possibly because β‐glucan bioactivity is reduced in some food products by low solubility or low molecular‐weight (MW). We tested whether daily consumption of 3g OBG (OatWell®) reduced LDL and whether LDL lowering depended on log(MW×C), where C = dose×solubility. Healthy subjects (n=367) with LDL ≥3.0 and ≤5.0mmol/L were randomly assigned to the following treatments in a double‐blind, parallel design clinical trial: 1.5g high‐MW (3H), 2g medium‐MW (4M), 1.5g medium‐MW (3M) or 2g low‐MW (4L) OBG in cereal or a control wheat fiber cereal (W) twice daily for 4wk. After 4wk on 3H LDL was less than on W by 0.21 mmol/L (95% confidence interval; 0.11, 0.30, P =0.0023). Analysis of covariance showed log(MW×C) was a significant determinant of week 4 LDL ( P =0.003). The size of treatment effects was not significantly affected by age, sex, centre or baseline LDL. We conclude that consuming 1.5g high‐MW OBG twice daily reduces LDL by 0.2mmol/L, but efficacy is reduced with OBG of lower MW and/or C. Thus, the physico‐chemical properties of β‐glucan influence oat's LDL‐lowering effect. Funded by the Swedish Governmental Agency for Innovations Systems and CreaNutrition.
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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) | 0.001 | 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".