Effect of semolina pudding prepared from<i>starch branching enzyme IIa</i>and<i>b</i>mutant wheat on glycaemic response<i>in vitro</i>and<i>in vivo</i>: a randomised controlled pilot study
Bibliographic record
Abstract
Refined starchy foods are usually rapidly digested, leading to poor glycaemic control, but not all starchy foods are the same. Complex carbohydrates like resistant starch (RS) have been shown to reduce the metabolic risk factors for chronic diseases such as hyperglycaemia and overweight. The aim of the project was to develop a semolina-based food made from a starch branching enzyme II (sbeIIa/b-AB) durum wheat mutant with a high RS content and to measure its glycaemic index using a double-blind randomised pilot study. We report here the amylose, RS and non-starch polysaccharide concentration of raw sbeIIa/b-AB and wild-type control (WT) semolina. We measured RS after cooking to identify a model food for in vivo testing. Retrograded sbeIIa/b-AB semolina showed a higher RS concentration than the WT control (RS = 4.87 ± 0.6 g per 100 g, 0.77 ± 0.34 g per 100 g starch DWB, respectively), so pudding was selected as the test food. Ten healthy participants consumed ∼50 g of total starch from WT and sbeIIa/b-AB pudding and a standard glucose drink. Capillary blood glucose concentrations were measured in the fasting and postprandial state (2 h): incremental area-under-the-curve (iAUC) and GI were calculated. We found no evidence of difference in GI between sbeIIa/b-AB pudding and the WT control, but the starch digestibility was significantly lower in sbeIIa/b-AB pudding compared to the WT control in vitro (C90 = 33.29% and 47.38%, respectively). Based on these results, novel sbeIIa/b-AB wheat foods will be used in future in vivo studies to test the effect of different RS concentrations and different food matrices on glycaemia.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".