Acute Effect of Diets Varying in Glycemic Index and Glycemic Load on Blood Glucose, Insulin and Measures of Oxidative Stress
Bibliographic record
Abstract
Background Epidemiological studies indicate that low glycemic index (GI) and glycemic load (GL) diets may decrease risk for the development of coronary heart disease (CHD) and diabetes. Clinical evidence indicates that elevations in postprandial glycemia is associated with increased oxidative stress. As such, dietary strategies which limit postprandial glycemia may be of benefit in moderating oxidative damage. We assessed the effect of foods varying in GI and GL on postprandial glycemia, insulinemia and measures of oxidative damage. Methods Fifteen healthy subjects were fed 5 test meals: 50g available carbohydrate from white bread (control), which was tested twice; 50g available carbohydrate from pasta; 100g available carbohydrate from pasta; and 100g available carbohydrate from white bread. Blood samples were obtained over the following 4 hours. Results Compared to the white bread control (50 g available carbohydrate), the area under the 2‐hour glycemic response curve was significantly greater for 100g available carbohydrate from white bread (mean±SE) 141±15 (P<0.05); was significantly reduced for 50g available carbohydrates from pasta (71±6; P<0.05); and was unchanged for 100g available carbohydrates from pasta (90±10; P<0.05). Conclusion Altering the glycemic index and glycemic load of the diet can greatly affect the postprandial glycemic response curve, which may have implications for oxidative damage and risk of CHD and diabetes.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".