The effect of oat β-glucan on postprandial blood glucose and insulin responses: a systematic review and meta-analysis
Bibliographic record
Abstract
To determine the effect of oat β‑glucan (OBG) on acute glucose and insulin responses and identify significant effect modifiers we searched the MEDLINE, EMBASE, and Cochrane databases through October 27, 2020 for acute, crossover, controlled feeding trials investigating the effect of adding OBG (concentrate or oat-bran) to carbohydrate-containing test-meals compared to comparable or different carbohydrate-matched control-meals in humans regardless of health status. The primary outcome was glucose incremental area-under-the-curve (iAUC). Secondary outcomes were insulin iAUC, and glucose and insulin incremental peak-rise (iPeak). Two reviewers extracted the data and assessed risk-of-bias and certainty-of-evidence (GRADE). Data were pooled using generic inverse-variance with random-effects model and expressed as ratio-of-means with [95% CIs]. We included 103 trial comparisons (N = 538). OBG reduced glucose iAUC and iPeak by 23% (0.77 [0.74, 0.81]) and 28% (0.72 [0.64, 0.76]) and insulin by 22% (0.78 [0.72, 0.85]) and 24% (0.76 [0.65, 0.88]), respectively. Dose, molecular-weight, and comparator were significant effect modifiers of glucose iAUC and iPeak. Significant linear dose-response relationships were observed for all outcomes. OBG molecular-weight >300 kg/mol significantly reduced glucose iAUC and iPeak, whereas molecular-weight <300 kg/mol did not. Reductions in glucose iAUC (27 vs 20%, p = 0.03) and iPeak (39 vs 25%, p < 0.01) were significantly larger with different vs comparable control-meals. Outcomes were similar in participants with and without diabetes. All outcomes had high certainty-of-evidence. In conclusion, current evidence indicates that adding OBG to carbohydrate-containing meals reduces glycaemic and insulinaemic responses. However, the magnitude of glucose reduction depends on OBG dose, molecular-weight, and the comparator.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".