The Effect of a Breakfast Rich in Slowly Digestible Starch on Glucose Metabolism: A Statistical Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Starch digestibility may have an effect on the postprandial blood glucose profile. The aim of this meta-analysis was to analyze the relationship between Slowly Digestible Starch (SDS) levels and plasma glucose appearance and disappearance rates, as well as other parameters of glucose metabolism, after healthy subjects consumed cereal products that differed in SDS content. Three randomized controlled clinical trials that included a total of 79 subjects were identified. Using binary classification for the variables (high versus low levels, more than 12 g of SDS per portion, and less than 1 g of SDS per portion, respectively), we found that there was a 15-fold higher chance of having a low rate of appearance of exogenous glucose (RaE) after consumption of a high-SDS product. A high SDS content was also associated with a 12-fold and 4-fold higher chance of having a low rate of disappearance of exogenous glucose (RdE) and rate of disappearance of total plasma glucose (RdT), respectively. The RaE kinetics were further analyzed by modeling the contribution of SDS content to the different phases of the RaE response. We show that the higher the SDS content per portion of cereal product, the higher its contribution to the incremental area under the curve (iAUC) of the RaE response after 165 min. Using the association rule technique, we found that glycemic iAUC and insulinemic iAUC values vary in the same direction. In conclusion, this meta-analysis confirms the effect of the SDS level in cereal products on the metabolic response, and shows for the first time that the degree to which SDS affects the RaE response differs depending on the SDS content of the food product, as well as the phase of the postprandial period.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.052 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".