Effect of Novel Maize‐based Dietary Fibers on Postprandial Glycemia
Bibliographic record
Abstract
Background : Postprandial hyperglycemia has been associated with the development of diabetes, heart disease and all‐cause mortality. Therefore, diets that limit postprandial glycemia may be of benefit. Adding novel maize‐based dietary fibers to foods and beverages may prove beneficial in limiting postprandial glycemia. Objective : To assess the effect of novel maize‐based dietary fibers on postprandial glycemia. Methods : In two studies, 10 healthy volunteers were fed test beverages containing maize‐based fiber ingredients (25g total carbohydrate; ~10–20g dietary fiber) and 3 control meals (25g available carbohydrate) on separate occasions in random order. Capillary blood samples were obtained and relative glycemic responses (RGR) were assessed (IAUC). Results : Study 1: the RGR of the test F4‐763 fiber (3.4±1.3), and F4‐800 (44.5±4.5) and F4‐807 (51.7±5.9) soluble fibers were significantly less (P<0.05) than glucose (100) and the 10 DE maltodextrin beverage (111.1±10.3). Study 2: all six soluble fiber test beverages F4‐810 GR(1–6) (32.6±3.8; 23.2±4.6; 26.2±4.2; 15.3±3.6; 25.4±4.3; 18.2±3.5, respectively) had a significantly (P<0.05) reduced RGR compared with the glucose control (100). In study 2, there was no difference in palatability of the test beverages and the control (glucose). Conclusions : The use of maize‐based dietary fibers can effectively reduce the postprandial glycemic response of beverages without altering palatability. Utilizing foods containing these novel carbohydrates may be an effective way to limit postprandial glycemia thereby helping to control diabetes, heart disease and possibly body weight. Research Support: Tate & Lyle
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".