High-Fiber Cereal Reduces Postprandial Insulin Responses in Hyperinsulinemic but not Normoinsulinemic Subjects
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compare the plasma glucose and insulin responses elicited by two ready-to-eat breakfast cereals (one being high and the other being low in cereal fiber) and to see if the differences in response depended on subjects' fasting plasma insulin. RESEARCH DESIGN AND METHODS: Nondiabetic men (n = 77) were studied on two occasions after 10- to 14-h overnight fasts. They consumed 25 g of available carbohydrate from high- or low-fiber breakfast cereals in random order with blood taken at intervals for 2 h. Data from the 42 men with high fasting plasma insulin (FPI) on screening (>40 pmol/l) were compared with those from the 35 men with normal FPI (< or =40 pmol/l). RESULTS: Hyperinsulinemic men had significantly higher waist circumference and BMI, lower HDL cholesterol, and a trend toward higher triglycerides (P = 0.07) than control subjects. In all 77 subjects, the incremental area under the glucose response curve (AUC) after high-fiber cereal was 11.8 +/- 5.5% (P = 0.036) less than after low-fiber cereal with the reductions being equivalent in the hyperinsulinemic (12.6 +/- 8.3%) and control (10.9 +/- 9.1%) groups. However, insulin peak rise was reduced by the high-fiber cereal only in hyperinsulinemic men (351 +/- 29 vs. 485 +/- 55 pmol/l) but not in control subjects (211 +/- 20 vs. 220 +/- 20 pmol/l; cereal x group interaction P = 0.044). Insulin AUC after the high-fiber cereal, expressed as a percentage of that after low-fiber cereal, was negatively related to FPI (P = 0.009) but not to age, BMI, or waist circumference. CONCLUSIONS: The high-fiber cereal reduced glucose responses to the same extent in normal and hyperinsulinemic men, but reduced insulin responses only in hyperinsulinemic subjects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".