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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it