The effect of oat<i>β</i>-glucan on LDL-cholesterol, non-HDL-cholesterol and apoB for CVD risk reduction: a systematic review and meta-analysis of randomised-controlled trials
Bibliographic record
Abstract
Oats are a rich source of β-glucan, a viscous, soluble fibre recognised for its cholesterol-lowering properties, and are associated with reduced risk of CVD. Our objective was to conduct a systematic review and meta-analysis of randomised-controlled trials (RCT) investigating the cholesterol-lowering potential of oat β-glucan on LDL-cholesterol, non-HDL-cholesterol and apoB for the risk reduction of CVD. MEDLINE, Embase, CINAHL and Cochrane CENTRAL were searched. We included RCT of ≥3 weeks of follow-up, assessing the effect of diets enriched with oat β-glucan compared with controlled diets on LDL-cholesterol, non-HDL-cholesterol or apoB. Two independent reviewers extracted data and assessed study quality and risk of bias. Data were pooled using the generic inverse-variance method with random effects models and expressed as mean differences with 95 % CI. Heterogeneity was assessed by the Cochran's Q statistic and quantified by the I 2-statistic. In total, fifty-eight trials (n 3974) were included. A median dose of 3·5 g/d of oat β-glucan significantly lowered LDL-cholesterol (-0·19; 95 % CI -0·23, -0·14 mmol/l, P<0·00001), non-HDL-cholesterol (-0·20; 95 % CI -0·26, -0·15 mmol/l, P<0·00001) and apoB (-0·03; 95 % CI -0·05, -0·02 g/l, P<0·0001) compared with control interventions. There was evidence for considerable unexplained heterogeneity in the analysis of LDL-cholesterol (I 2=79 %) and non-HDL-cholesterol (I 2=99 %). Pooled analyses showed that oat β-glucan has a lowering effect on LDL-cholesterol, non-HDL-cholesterol and apoB. Inclusion of oat-containing foods may be a strategy for achieving targets in CVD reduction.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".