The Effect of Oat Beta‐Glucan on Clinical Lipid Markers for Cardiovascular Disease Risk Reduction: A Systematic Review & Meta‐Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background Oats are a rich source of β‐glucan, a viscous soluble fibre recognized for its cholesterol lowering properties, and are associated with reduced risk of cardiovascular disease. Objective To conduct a systematic review and meta‐analysis of RCTs investigating the cholesterol lowering potential of oat β‐glucan on LDL‐C, non‐HDL‐C, and apoB, for the risk reduction of cardiovascular disease. Design MEDLINE, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials were searched. We included Only RCTs of ≥ 3 weeks duration assessing the effect of diets enriched with oat β‐glucan compared with controlled diets on LDL‐C, non‐HDL‐C, or apoB. Two independent reviewers extracted relevant 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 (MD) with 95% confidence intervals (CIs). Heterogeneity was assessed by the Cochran Q statistic and quantified by the I 2 statistic. Results 58 trials (N = 3952) were included in the final analysis. A median dose of 3.5 g/day of oat β‐glucan significantly lowered LDL‐C (MD = −0.19 mmol/L [95% CI: −0.23, −0.14] P < 0.00001) non‐HDL‐C (MD = −0.20 mmol/L [95% CI: −0.26, −0.15] P < 0.00001) and apoB (MD = −0.03 g/L [95% CI: −0.05, −0.02] P < 0.0001) compared to control interventions. There was evidence of considerable unexplained heterogeneity in the analysis of LDL‐C (I 2 = 79%) and non‐HDL‐C (I 2 = 99%). Conclusion Pooled analyses show that oat β‐glucan have a lowering effect on LDL‐C, non‐HDL‐C, and apoB. Inclusion of oat containing foods may be a strategy for achieving targets in cardiovascular disease reduction.
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How this classification was reachedexpand
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 | 0.177 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.078 | 0.139 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".